text:
\section{Introduction}

Do experts have differential expertise? That is, can we grade and
rank experts by the quality of their expertise? To put these
questions in context, an expert is someone who knows about a domain
more than is commonly known. Often, this manifests itself in being able to predict
the outcome of a future event more accurately than laymen. The expert's level of
expertise is his expected accuracy, which, assuming the expertise is
real, may be learned from his past record of predictions. If there
is no correlation between an expert's past record and how well he
will do in his next prediction, the expert's results are random, and
there is no expertise.

The alternative to the existence of differential expertise is that
all experts (on a particular domain) have the same expected
accuracy. This can plausibly arise when all experts share a successful model (e.g., in football, that there is a home team advantage,
and that the last 3-months win-lose record is a good indicator of future success),
while any differences among them stem from random noise (for example, holding unfounded beliefs, such as that a particular team performs better in
large stadiums).

If differential expertise does not exist, the best way to
advantageously aggregate predictions of several experts, is to
average them. This reduces the noise element (variance) within
individual predictions, highlighting the common signal. On
the other hand, if experts do have differential expertise, then
averaging predictions, while usually still advantageous, is sub-optimal: A
weighted average, in which the better experts are given more
weight than their colleagues, should, in expectation, produce a
more accurate aggregate prediction.

We turn this argument on its head in order to measure the amount of differentiation in expertise:
Informally, the bigger the differentiation, the more unequal will be the weights assigned to experts, and
the larger the accuracy improvement (compared to unweighted aggregate) prospects of the prediction.
We {\em define} the amount of differentiation as the accuracy improvement that is achievable by weighting. To make this definition practical
and precise, (the best weighting method may be unknown), we restrict ourselves to weights calculated by a simple and reasonable algorithm,
whose results, if not optimal, are expected to be at least in the same order of magnitude. This will be sufficient for us to make {\em qualitative}
judgements about expert differentiation (e.g. ``Differentiation is large / modest / insignificant'').

We conducted an empirical study of this question, using earnings of
publicly-traded companies as the domain of predictions. The experts
here are stock-market analysts, and the outcomes are announced by
the companies on a quarterly or annual basis, on dates set well in
advance. There are several advantages to choosing this domain:
First, databases with historical predictions and results are
available for the past several decades. Second, analyst predictions,
and especially the ``surprise'', the difference between analyst
consensus\footnote{The consensus is defined on the NASDAQ website as
``The average EPS estimates for the company from the universe of
analysts reporting for the company \ldots''} and actual results, are
known to be major drivers of stock prices, so that these predictions
attract much interest from investors, and, due to the large amount
of money at stake, can be assumed to have best effort invested in
them. Third, much academic work has been done in the past half
century on the accuracy of analysts and what affects it.

To answer our question about expert differentiation we employ
algorithms that combine a weighted consensus estimate from individual predictions. The algorithms are not presented
as innovative or optimal, (indeed, the major part of their improvement is achieved with an algorithmically-trivial bias correction),
but are competent enough to show the order-of-magnitude improvement achievable.

The contribution of our work is in three areas:
\begin{enumerate}
\item We clarify the \cite{sinha1997re} result, described below, that found that analysts differ in accuracy, but did not quantify the size of this differentiation. We confirm the existence of accuracy differentiation, but show that it is rather modest, and of doubtful economic significance. In the process, we make the first aggregation of analyst estimates. The absence of published aggregation attempts in the curious and motivated academic community following Wall Street is puzzling. Lack of significant results may be the explanation, or perhaps secretiveness.
\item The technique of devising improved weighted estimates is suggested as
a definite test for the existence and magnitude of differentiation among experts, and
the sensitivity of its results to changes of parameters is suggested as a method for model selection. The superiority of this method over statistical tests of significance is demonstrated. This is applicable to other fields where
multiple predictions are made: Sports competitions, election results, weather and climate forecasting,
revenue forecasting for new movies / books / albums, and more.
\item Despite the minor differentiation in expertise among analysts, we nevertheless do achieve a significant average improvement of 20\%-30\% in forecast accuracy over consensus. The major part of this improvement comes from an unexpected quarter: Systematic and individual analyst bias. While a general pessimistic bias is well-known (see below), we find a significantly larger {\em individual} analyst bias. This discovery was an unanticipated part of our work. The literature has struggled to explain the general bias of analysts (since self-calibration is the easiest part of prediction). The discovery of significant individual bias may shed light on this.
\end{enumerate}

\subsection{Related Literature}
\label{related}

In \cite{goel2010prediction}, the authors investigate the advantage
of market mechanisms over ``conventional methods of prediction,
namely polls and statistical models'' in several domains, and find a
``surprisingly small'' advantage to the markets, and ``remarkably
steep diminishing returns to information, with nearly all the
predictive power captured by only two or three parameters''.

Though the study does not ask the same questions we ask, it supports
the conclusion that differential expertise is either non-existent or
weak, in the domains investigated. This is because, as the authors
note, ``theoretical and empirical evidence suggests that markets do
often outperform alternative mechanisms''. If markets do not
strongly outperform ``two or three parameters'' models, then perhaps
nothing does.

A vast literature has analyzed analyst performance and the factors
affecting it for the past half century. For a survey, see, e.g.,
\cite{ramnath2008financial}. The consensus regarding differential
accuracy of analysts has gone through changes. \cite{o1990forecast}
studied the statistical relationship of analyst accuracy to the
identity of the analyst, the firm, and the year, and in regards to
the analyst concluded ``Overall, I find no systematic differences in
forecast accuracy across individuals''.

However, \cite{sinha1997re} repeated O'Brien's work and
overturned its conclusion by adding another element of variation.
Paraphrasing, they note that analyst forecasts are heavily
influenced by events, or shocks, which also causes them to change in
near-unison. When this effect, modeled as a time series, is added to
O'Brien's independent variables, the
identity of the analyst re-emerges as significantly affecting
forecast accuracy.

{\em Despite much further work on analyst accuracy and the factors affecting it, and despite suggestions to clarify its results (e.g., in  \cite{ramnath2008financial}), \cite{sinha1997re} remains the latest word on whether forecast accuracy differs among analysts. No later work attempts to quantify the differentiation}.

O'Brien, Sinha, and almost the entire literature on analyst earnings predictions aim to prove or disprove an underlying relationship to various attributes using statistical tests of significance. We consider tests of significance as inadequate for proving differential expertise, first, because statistical significance does not imply economic significance, and second, because it attaches no numerical magnitude to the differentiation. As opposed to the cited works, we develop a methodology of {\em weighting} to measure differentiation of expertise, then use its results to draw conclusions.

As noted, differences in expertise should make it possible to cast more accurate predictions by suitable weighting. Indeed, we argue that the ability to cast a significantly better prediction is a better test of significance, since, while clearly if a relationship is statistically insignificant it cannot lead to better predictions by weighting, the converse is not true: A statistically-significant relationship may lead to only negligibly better predictions through weighting.

Notwithstanding, with the exception of two works to be presently cited, we have not found any attempt to leverage proved relationships into more accurate consensus predictions. The survey \cite{ramnath2008financial} cites no such work, and merely suggests it for future research.

The latter of the two works, \cite{slavin2007aggregating}, (Slavin, henceforth), is based on, and improves on the earlier one \cite{brown2001profiting}, (BM, henceforth). Slavin claims to reduce forecast errors by 29\%, better than the 16\% achieved by BM. BM predict individual analyst accuracy using characteristics such as forecast recency, forecast frequency, brokerage reputation, experience, and the number of companies and industries covered. All these had been shown by even-earlier studies to be correlated with analyst accuracy. They then combine forecasts using weights that are based on each analyst's projected accuracy ranked relative to their peers. Slavin improves on their methodology with two steps. First, he uses past accuracy (in addition to their six factors) to predict individual analyst accuracy. Second, his forecast weight accounts for the magnitude of analyst accuracy, whereas BM use only ordinal ranking.

We use a modified version of their methodology in our present work, to model the individual expertise of analysts. (We supplement it with a model of individual bias). For us, it achieves only a fraction of the improvement reported above. There are many important differences between our data and implementation, and theirs, so this cannot be considered a reexamination of their studies. Nevertheless, we believe that the difference is too large to be explained by differences in details. If Slavin and BM ever produced results as good as they reported, then they no longer do, with more recent data. The reason we find their framework nevertheless useful is:

\begin{itemize}
\item The methodology, as described, is free of errors, such as inadvertently ``peeking'' into the future.
\item It uses most characteristics reported in the literature to be significantly correlated with analyst accuracy.
\item It seems unlikely that a different algorithm can gain much more from the same data, so what it achieves is a first-order approximation to what is achievable.
\end{itemize}

Better aggregation algorithms are probably available, for example \cite{raykar2009supervised}, \cite{raykar2010learning}. However, (i) they do not use domain-specific data. As noted, the literature on analyst performance has proven several attributes as significantly correlated with analyst accuracy. We cannot omit such proven attributes from our aggregation methods without putting the relevance of our work into question. {\em Inter alia}, our methodology will show that some of these attributes in fact have insignificant marginal impact. And (ii) As we argue later, an aggregation algorithm would need to be implausibly an order-of-magnitude better to change our qualitative conclusions.

In regards to our findings on analyst bias: Surprisingly, over the last three to four decades there has been a
consistent drift from slightly more negative surprises (i.e., analyst optimism) to
overwhelmingly positive surprises (i.e., analyst pessimism, see \cite{Brown2001} and
\cite{Matsumoto2002} for a documentation of the drift). The academic
financial research has struggled to explain the sources  for
this drift. Possible explanations include analysts' conflict of
interest (\cite{ChanKaLa2007}), management avoidance of negative
surprises (\cite{Matsumoto2002}), difference among various countries
(\cite{BrownHi2001}), and more.
Our findings are in a similar spirit, where the positive surprises
outnumber the negative ones.

\subsection{Summary of Results}

We describe an algorithm that calculates an improved consensus prediction out of the set of all analyst predictions preceding a quarterly earnings announcement, and we run it for all quarters in 2010-2016 for all firms who compose two well-known indices: The S\&P-500 index, and the NASDAQ-100 index.

We use the resulting accuracy of this improved consensus as a benchmark for significance, eschewing statistical tests of significance. This is because we note that, often, attributes that test positively for significance nevertheless have insignificant impact on our consensus prediction.

Measuring accuracy by the criterion used by Slavin and BM, the median improvement in surprise, we find an improvement of 21\% in the S\&P-500 group and 28\% in the NASDAQ-100 group.

Our conclusion, though, is that analyst differential expertise is modest. This is because the major part of the improvement stems from modeling individual bias. The rest, and much smaller part, comes from modeling individual expertise and other attributes. Modeling individual bias {\em only} improves estimates by 15\% for the S\&P-500 group and 22\% for the NASDAQ-100 group. Modeling individual expertise {\em only} (the Slavin / BM methodology) improves the S\&P-500 group by a mere 3\%, and the NASDAQ-100 group, by 5\%.

We find that bias is best modeled as a function of both firm and analyst. This yields much better results than correcting for a general bias, and is also superior to modeling bias as dependent on firm only, or on analyst only, or on any weighted combination of both. The conclusion is that analysts have a consistent {\em individual}  bias, quite apart from the general pessimistic bias displayed by the aggregate of analyst opinions.

If analyst identities are replaced by their institutions (brokerages), accuracy decreases. This indicates that individual bias and expertise are related to the analyst rather than to the employing institution.

\subsection{Organization of this Paper}

The rest of this paper is organized as follows. In Section \ref{data} \textsc{Data} we describe the data used. In Section \ref{methods} \textsc{Methods} we describe the methods and algorithms used. Results are presented in Section \ref{results} \textsc{Results}. In Section \ref{discussion} \textsc{Discussion} we summarize and offer concluding remarks.

\section{Data}
\label{data}

The data for this study were obtained from the Thomson Financial's Institutional Broker Estimate System (I/B/E/S), available through Wharton Research Data Services. Specifically the databases used were Detail History / Detail, Detail History / Actuals and Summary History / Surprise History.

Data was collected for two stock symbol lists\footnote{Companies occasionally enter or leave these symbol lists. Note that while we use the lists as of 2016, we use the record of the component companies over all the study history, whether or not they were part of the index at the time.}:
\begin{enumerate}
\item The S\&P-500 index, as of 2016 (504 symbols).
\item The NASDAQ-100 index, as of 2016 (103 symbols).
\end{enumerate}

Data was gathered on analyst estimates and actual results for all earnings announcements made between 2004 and 2016. We calculated our improved consensus prediction only for results announced 2010-2016, since we wanted it to rely on at least 6 years of analyst performance history.

For each quarterly earnings announcement, the actual earnings were taken from both the Actuals and the Surprise History datasets. Earnings where the two values did not match were discarded. To exclude probable errors, earnings with absolute surprise of more than 50 cents per share were also discarded. This caused less than 5\% of the reports to be excluded. Also, only earnings announcements which at least 8 different analysts estimated were considered. Analyst estimates made within a year of the earnings announcement (specifically, having I/B/E/S field FPI = 6, 7, 8 or 9), and no later than 48 hours\footnote{Later estimates are suspected to be errors in data.} before the announcement, were considered. If an analyst issued several estimates for the same earnings announcement, only the last one was considered. Also excluded were predictions where the analyst had no record of previously estimating the firm. This guarantees that data exists to estimate past accuracy.

\section{Methods}
\label{methods}

For readability, we describe our methods verbally. For added clarity, the reader will find formulas in the Appendix.

We number the periods (quarters) under study $1,2,...$ in chronological order. Let $ACTUAL_{j,t}$ be the actual earnings of firm $j$ at period $t$, and, if analyst $i$ made a prediction of it, let $PREDICT_{i,j,t}$ be that prediction.

The signed prediction error of analyst $i$ of firm $j$ for period $t$, $ERR_{i,j,t}$, is the difference between the analyst prediction for the period and the actual earnings.

The experience at $t$ of analyst $i$ with firm $j$ is the number of periods prior to $t$ that the analyst made a prediction for the firm.

\subsection{Bias Calculation}

Bias is tracked for each analyst-firm combination. $BIAS_{i,j,t}$ is the bias of analyst $i$ for firm $j$ at time $t$. It is defined as the average of the prediction error $ERR_{i,j,\tau}$ for all periods previous to $t$ where the analyst made a prediction for firm $j$. If there are no such periods, the bias is defined to be $0$.

The bias-adjusted prediction of an analyst is his prediction, minus his bias at the current period. Similarly $AE_{i,j,t}$, the adjusted prediction error is the prediction error minus the current bias.

\subsection{Modeling Prediction Errors}
\label{model}

Following \cite{slavin2007aggregating} (see Section \ref{related} \textsc{Related Literature}, where we explained our rationale for following that framework), we best-fit a multiple linear model to prediction data in every calendar quarter in our study (2010-2016), then use the resulting model to predict individual prediction errors in the {\em next} quarter. These predicted errors are then used to attach weights to individual analyst predictions, (the smaller the predicted error, the larger the weight). Our improved consensus estimate is the resulting weighted average of analyst estimates.

The reader should bear in mind our intended conclusion: The results of this aggregation algorithm (without bias adjustment) are {\em not significant}, achieving, as shown below, a 3\%-5\% accuracy improvement over simple consensus. This is less than 1/5 of the total improvement achieved, the rest attributable to bias adjustment. The algorithm is not presented as optimal, but as ``good enough'', and, according to existing literature, using all relevant data. Better algorithms might yield better results,  but not enough to change the conclusion, which is that expertise differentiation among analysts is modest.

There are some differences between our model and Slavin's, the major one being that we adjust all predictions for individual bias. Other differences are listed in the Appendix.

\subsubsection{Variables}
\label{variables}

The dependent variable, $AAE_{i,j,t}$, is the absolute bias-adjusted prediction error of analyst $i$ of firm $j$ for period $t$.

The independent variables are:
\begin{enumerate}
\item $AGE_{i,j,t}$: The number of days from the prediction to the earnings announcement.
\item $FREQ_{i,j,t}$: The number of predictions this analyst has made for the current quarter.
\item $NCOS_{i,j,t}$: The number of different firms this analyst is covering in this quarter.
\item $TOP10_{i,j,t}$: $1$ if the institution for which this analyst works is in the top decile of institutions, $0$ if not, where institutions are ranked by the number of analysts reporting for them in the current quarter.
\item $EXP_{i,j,t}$: The number of times this analyst previously reported on this firm.
\item $MAE_{i,j,t}$: Mean of $AAE_{i,j,\tau}$ in all this analyst's previous predictions for this firm. (Earnings for which there are no such previous predictions are excluded).
\end{enumerate}

The variables $DAAE_{i,j,t}$, $DAGE_{i,j,t}$, $DFREQ_{i,j,t}$, $DNCOS_{i,j,t}$, $DTOP10_{i,j,t}$, $DEXP_{i,j,t}$ and $DMAE_{i,j,t}$ are versions of the above, normalized and scaled to their all-analyst average: e.g. $DAGE_{i,j,t} = AGE_{i,j,t} / AGE_{j,t} - 1$ ($AGE_{j,t}$ stands for the all-analyst average of $AGE_{i,j,t}$).

\subsubsection{Linear Model}
\label{linear-model}

Our multiple linear model is
\begin{eqnarray}
\label{best-fit}
DAAE_{i,j,t}^* = & \beta_{1,t }DAGE_{i,j,t} +  \beta_{2,t} DFREQ_{i,j,t}  \nonumber \\
&+ \beta_{3,t} DNCOS_{i,j,t} + \beta_{4,t} DTOP10_{i,j,t}  \nonumber \\
&+ \beta_{5,t} DEXP_{i,j,t}  + \beta_{6,t} DMAE_{i,j,t} + \epsilon_{i,j,t}
\end{eqnarray}

For each period $t$, multiple linear regression is used to find $\beta_{k,t}, k= 1, \ldots, 6$ that best-fit this model, minimizing the sum of squares of the errors $\epsilon_{i,j,t}$: $$\sum_{i,j} (DAAE_{i,j,t}^* - DAAE_{i,j,t})^2$$

A prediction of $DAAE_{i,j,t}$, marked $\widehat{DAAE}_{i,j,t}$, can then be calculated from {\em current} attributes and the {\em previous} period's model, as follows
\begin{eqnarray}
\label{predict-error}
\widehat{DAAE}_{i,j,t} = & \beta_{1,t -1}DAGE_{i,j,t} +  \beta_{2,t-1} DFREQ_{i,j,t}  \nonumber \\
& +  \beta_{3,t-1} DNCOS_{i,j,t} +  \beta_{4,t-1} DTOP10_{i,j,t} \nonumber \\
&+  \beta_{5,t-1} DEXP_{i,j,t}  + \beta_{6,t-1} DMAE_{i,j,t}
\end{eqnarray}

\subsubsection{Calculating a Weighted Consensus}
\label{weights}

Our consensus prediction $PREDICTION_{j,t}$ is a weighted average of analyst $i$ predictions for firm $j$ in period $t$. The weights are set as follows:

For each analyst $i$ reporting on firm $j$ in period $t > 1$, the
predicted error is calculated by (\ref{predict-error}), using model \eqref{best-fit} that was best-fit for the {\em previous} period. Then the
average (over analysts) predicted error $\widehat{DAAE}_{j,t}$ is
calculated. Analysts whose predicted error exceeds the all-analyst average
are given zero weight, while others are given
weight $ (\widehat{DAAE}_{j,t} - \widehat{DAAE}_{i,j,t})^r$. The result is not very sensitive to $r$. We use $r=1.2$, which we find fit the data better than Slavin's $r=2$ (see Table \ref{result-table}).

\section{Results}
\label{results}

\subsection{Statistics Used}

Our results compare how our weighted consensus prediction $PREDICTION_{j,t}$ measures against the simple-average consensus $CONSENSUS_{j,t}$. The simple-average consensus is widely used and reported by the financial community and popular websites, and so is the natural yardstick.

We use three different statistics to evaluate the total improvement of the result for a given list of firms.
\begin{itemize}
\item \textsc{median}: Defined as the median, taken over all firms $j$ in the list and all periods $t$ in the target range, of $SURPIMPR_{j,t}$, the fractional improvement in the absolute surprise.

\item \textsc{average}: This is the ratio of the sum of all improved absolute surprises divided by the sum of the original absolute surprises.

\item \textsc{trend}: This is $1$ minus the slope of the linear regression of the improved surprise $PREDICTION_{j,t} - ACTUAL_{j,t}$ vs. the original surprise $CONSENSUS_{j,t} - ACTUAL_{j,t}$ (see Figure \ref{trend}).

\end{itemize}

The \textsc{median} statistic is not sensitive to the magnitude of the stock price. On
the other hand the \textsc{average} statistic might over-emphasize high stock prices,
assuming earnings and surprises are in proportion. The \textsc{median} statistic over-emphasizes announcements with low surprise (for zero surprise $SURPIMPR_{j,t}$ is infinite), but,
being ordinal, the effect is not excessive. On the other hand the \textsc{average} statistic, in effect, weighs
announcements by surprise, so under-emphasizes announcements with low surprise.

The \textsc{trend} statistic is the only one that takes into account the sign of the surprise. See Figure \ref{trend}. However, we consider this statistic to be less trustworthy than the other two, and do not use it for mode sensitivity analysis. This is because it is sensitive to outliers, and is meaningless when the correlation coefficient is low.

\subsection{The Results}

\begin{table*}[tb]
  \caption{Statistics on Earnings Reports}
  \label{stats-table}
  \centering
 \begin{tabular}{lrr}
\toprule
& S\&P-500 & NASDAQ-100 \\
\midrule
Number of symbols & 504 & 103 \\
Number of reports & 10,776 & 2,126 \\
Number of predictions & 434,255 & 102,217 \\
Number of analysts & 5,711 & 2,701 \\
Average absolute surprise (cents per share) & 7 & 7.2 \\
Median absolute surprise (cents per share) & 3.8 & 3.2 \\
Reports with negative surprise & 30.3\% & 27.8\% \\
Reports with actual in prediction range & 75.4\% & 73.9\% \\
\bottomrule
\end{tabular}
\end{table*}

\begin{table*}[tb]
  \caption{Improvement Results for Method and Variations}
  \label{result-table}
  \centering
 \begin{tabular}{lrrrrrr}
\toprule
&
\multicolumn{3}{c@{\quad}}{S\&P-500}
&
\multicolumn{3}{c}{NASDAQ-100} \\\cmidrule(r){2-4}\cmidrule(l){5-7}
Mode &\textsc{median} & \textsc{average} & \textsc{trend}  &\textsc{median} & \textsc{average} & \textsc{trend}   \\
\midrule
Full mode & 20.9\% & 16.7\% & 24.6\% & 27.7\% & 23.9\% & 28.9\% \\
\\
Without individual expertise & 14.7\% & 9.9\% && 22.2\% & 12.8\% \\
Without individual bias & 2.6\% & 4.8\% && 5.2\% & 12.1\%\\
\\
Without $AGE$ & 19.0\% & 14.5\% && 26.0\% & 20.2\% \\
Without $FREQ$ & 20.8\% & 16.6\% && 27.8\% & 24.0\% \\
Without $TOP10$ & 20.9\% & 16.7\% && 27.9\% & 23.9\% \\
Without $NCOM$ & 20.9\% & 16.7\% && 27.1\% & 24.0\% \\
Without $EXP$ & 20.5\% & 16.6\% && 27.4\% & 24.0\%\\
Without $MAE$ & 17.9\% & 14.3\% && 26.2\% & 22.6\% \\
Without scaling of variables & 19.4\% & 15.5\% && 27.2\% & 22.3\%  \\
\\
General bias  &14.5\% & 11.4\% && 19.8\% & 17.5\% \\
Bias based on firm only &16.9\% & 13.1\% && 25.8\% & 22.1\% \\
Bias based on analyst only & 18.1\% & 14.7\% && 24.8\% & 20.8\% \\
Bias weighted half-firm, half-analyst & 19.5\% & 15.7\% && 25.8\% & 23.0\% \\
\\
Use institution instead of analyst id & 19.8\% & 16.2\% && 27.6\% & 21.3\% \\
Use Slavin's exponent in Section \ref{weights} & 20.2\% & 16.0\% && 27.0\% & 24.2\% \\
Estimates up to 30 days before earnings & 16.1\% & 12.1\% && 23.8\% & 19.1\% \\
Estimates up to 60 days before earnings & 13.0\% & 9.1\% && 21.4\% & 17.6\% \\
\\
Closest analyst & 90.0\% & 80.0\% && 93.2\% & 85.5\% \\
Closest analyst without bias correction & 96.4\% & 70.8\% && 100.0\% & 76.6\% \\
\bottomrule
\end{tabular}
\end{table*}

\begin{figure*}[!tbp]
  \centering
  \begin{minipage}[b]{0.49\textwidth}
    \includegraphics[height=0.3\textheight]{trend-S_P-500}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.49\textwidth}
    \includegraphics[height=0.3\textheight]{trend-NASDAQ-100}
  \end{minipage}
\caption[Original and improved prediction errors with trend-line]{Original and improved prediction errors with trend-line}
\label{trend}
\end{figure*}

Table \ref{stats-table} lists several statistics about the earnings reports on which we ran this study.

Results are tabulated in Table \ref{result-table}. The three statistics are shown for the two firm groups. The first row has the results for the full mode, as described above. The other rows are a sensitivity analysis, showing results for various variations and omissions, as described in the {\em Mode} column.

With hundreds of earnings reports per period (see Table \ref{stats-table}), the number of adjustable parameters used in our model (6) and for model selection (see Table \ref{result-table}) is small in comparison. No overfitting takes place.

Figure \ref{trend} shows all predictions used in two graphs, one per symbol list. Each prediction used is represented by a blue circle. Its horizontal coordinate is the original surprise, and its vertical coordinate is the surprise improved by the model. The axes are placed at the average of the respective surprise. Note that their values are negative, reflecting the tendency of analysts to underestimate earnings. The red trend line is the result of simple linear regression on the prediction points, with the linear relationship and R-squared value in the upper left-hand corner. The linear regression is used to compute the \textsc{trend} statistic. The fact that the trend-line slope is less than 1 (angle less than \ang{45}) shows the general improvement of predictions by the model. The \textsc{trend} statistic is read as 1 minus the slope coefficient of the regression.

In general, we observe that results for the NASDAQ-100 group are consistently better than for the S\&P-500 group, indicating that for this group, differentiation among analysts is more significant. We also note that for all modes that include individual bias, the \textsc{median} statistic is higher than the \textsc{average}, indicating the presence of low-improvement outliers. The \textsc{trend} statistic yields the highest results.

The rows {\em Without individual bias} and {\em Without individual expertise} demonstrate that the major part of the method's improvement derives from modeling individual bias. Modeling individual expertise, i.e. Section \ref{model} \textsc{Model}, on its own, yields single-digit percentages in the \textsc{median} statistic,  less than one-fifth of the total.

Modes starting {\em Without $AGE$} show the effect of omitting each of the independent variables in Section \ref{variables} \textsc{Variables}, demonstrating its marginal contribution to accuracy. The effect is insignificant for $FREQ$, $TOP10$, $NCOM$ and $EXP$, showing that, by our criteria, these variables do not significantly affect the prediction accuracy. $AGE$ and $MAE$ are significant, as is the normalizing and scaling of the variables (Section \ref{variables} \textsc{Variables}).

Several modes check variations on correcting for bias based on analyst-firm combination. They show that correcting based on general (not analyst or firm-specific) bias is decidedly inferior. They also show that correcting based on analyst-only, on firm-only, or even using an average of the per-analyst bias and the per-firm bias, are all inferior. This is despite the fact that analyst-firm combinations have fewer samples, so that the related bias, calculated by averaging those, is more noisy.

If instead of modeling bias and expertise for individual analysts, we do the same for individual brokerages (the employers of the analysts), the results are inferior.

Cutting off predictions 30 or 60 days before earnings announcements results in significantly less improvement. This indicates that the differentiation among analysts is more significant for forecasts nearer earnings.

Finally, we check how good a ``prediction'' we could get if we followed the best analyst, in hindsight. This is tantamount to asking how close the closest analyst came to the actual result. The answer we find is, very close. Indeed, the $100\%$ \textsc{median} result for the NASDAQ-100 group means that in more than 50\% of these earnings announcements, there was an analyst who got it exactly right. In view of the other results, we find this result mildly surprising. It seems to show that our difficulty in predicting earnings is not so much because all analysts are far from the truth, but because we fail to identify in advance those who got it right. A related result shown in Table \ref{stats-table} is that about $75\%$ of actuals fall between the lowest and highest analyst prediction.

\section{Discussion}
\label{discussion}

\subsection{Conclusions}

We performed an empirical study of aggregating analyst predictions to an improved-accuracy consensus, and used its results to show that, while analysts have differential expertise, the differentiation is not large. We described in detail a methodology that shows an economically significant improvement of 20\%-30\% in accuracy over standard consensus methods. A side benefit of this methodology is that it allows us to gauge in a novel fashion the significance, or insignificance, of many parameters that are hypothesized to affect analyst accuracy. We use it to show, {\em inter alia}, that several analyst parameters considered significant to accuracy, are in fact of low marginal significance.

We find that analysts, and even more notably, analyst-firm combinations, have both individual expertise and individual bias. The individual bias part is surprisingly large, and economically important in its own right. We find it is significantly bigger than the general (not specific to an analyst or firm) bias, and much more significant than the individual expertise, which may not be economically significant on its own.

When analyst identities are replaced by those of their institutions, the importance of the individual element is reduced, indicating that differentiation is mainly among individuals rather than among their institutions.

\subsection{Beyond Earnings}

Looking beyond our subject of earnings estimates, our study, though confirming the existence of differential expertise, strengthens and confirms \cite{goel2010prediction}'s observation (see Section \ref{related} \textsc{Related Literature}) of ``remarkably
steep diminishing returns to information''. If not for the substantial contribution of individual bias, we would be unable to leverage decades of research into analyst performance to more than a few percentage points of improved accuracy.

On the other hand, when including bias, we found significant total differentiation among experts. Would it not be correct to call individual bias (more precisely, a lack of it) a form of individual expertise? After all, an expert with little or no bias is more accurate than one with a large bias, all else being equal. The problem with doing that is that systematic bias is akin to lack of calibration in binary predictions (e.g. when a forecaster attaches a 70\% probability to all events that actually happen 60\% of the time). Being well-calibrated is often considered as separate from being {\em precise}, which, in binary predictions, means successfully attaching probabilities that are far from the non-committal 50\%.

There is a good practical reason for this distinction: Systematic bias (like lack of calibration) is easily observed and corrected for (even self-corrected for), unlike other sources of inaccuracy. Our discovery of widespread and significant bias in the well-followed universe of earnings predictions is therefore surprising.

\subsection{Other Domains}

The question we ask, ``Are all experts equally good?'', and our follow-up question, "And if not, how different are they?", is relevant to other domains of prediction. The implication, of what the potential is for improving predictions by aggregation, also applies.

This would be of great interest, for these domains in particular, and for the general question of what experts, in general, know, and what they do not. Our methodology as explained in this paper can provide answers given the data. We have looked, with the aim of continuing our investigation, but as yet have not found other domains with suitably available data. This would require:

\begin{itemize}
\item A domain of predictions, preferably of continuous outcomes.
\item An archive of prediction outcomes.
\item An archive of individual predictions of one of these outcomes, made by identifiable experts in the domain, whether they call themselves that way or not, whether they are individuals or institutions, and whether they see themselves as making predictions or not. (A bettor making a bet in a horse race, for example, would suffice).
\item Several individual predictions should be made of each outcome, preferably many.
\end{itemize}

Possibly there are sources that are open to insiders, and may be available to researchers through some agreement. We would be interested.

\bibliographystyle{ACM-Reference-Format-Journals}

redpajama_set_name: RedPajamaArXiv


A set of 5 Wisdens, in a run from 1965 to 1969, all Linens.
The books are in Very Good condition, all 8s , there is and odd small mark here and there to the covers but internally very nice indeed, tony loss to rear corner of 1966 and the 1968 is very nice indeed.
Nice inside. More photos on request. May split, just ask. Other sets available in these years or others.
Payment Details: Card, BACS, Paypal or cheque please.


This super cool B is for Bike screen print by designer Anthony Oram is for riders who love their homes and their bikes! When not flying down hills on his bike, keen cyclist Anthony Oram likes to play with ideas and an obsession with type led to designing posters. Oram's fantastic cycling inspired prints are beautiful and will look splendid on any wall!


The Scottish Ambulance Service and Emergency Department provide care for people with symptoms of serious illness or who have been badly injured. When you call 999 the Scottish Ambulance Service will respond with the most appropriate help for your situation.
Of all the NHS services, 999 and the Emergency Department at Forth Valley Royal Hospital are services that should only be used for serious illnesses or injuries. This means that essential treatment is given to those who need it as quickly as possible.
If you are not in need of emergency or urgent treatment you should contact your GP or NHS 24 Out of Hours on 111. Our Minor Injuries Unit is also available for minor treatments from 9am – 9pm 7 days a week.


text:
Are You A Fast Food Addict?
The girl who only ate Chicken McNuggets...for 15 years
Imagine eating nothing but salt-filled McDonald's Chicken McNuggets. For 15 years. That's what Stacey Irvine, a 17-year-old factory worker from England, did—and the consequences have started catching up with her. But before you write off this story as just plain crazy, do you know how much salt you're taking in? And what it's doing to your health?
When Irvine's mother first took her daughter to a McDonald's restaurant 15 years ago, the Daily Mail reports, it was love at first bite. Since then, the British teen has eaten almost nothing but Chicken McNuggets at every meal. A diet like this not only lacks vital nutrients, it also serves up a dangerous amount of salt. A 10-piece order packs in 900 milligrams (mg) of sodium, which is more than half the salt you should consume in a single day.
Irvine recently collapsed at work and was rushed to the hospital struggling to breathe. She's home now, but the amount of salt she's been eating means she'll need to clean up her diet faster than a McDonald's cashier turns around an order at the drive-thru. All that salt can lead to an increased risk of heart attack or stroke, especially as she gets older.
"The food industry creates a preference for very salty foods with their products, then creates products to satisfy that preference, and it becomes a feedback loop," says David Katz, MD, founding director of Yale University's Prevention Research Center.
While most people aren't surprised to hear that a high-sodium diet raises blood pressure, most Americans would be downright shocked if they knew how much salt they were really eating. The US government recommends that adults consume no more than 1,500 mg of sodium a day, about two-thirds of a teaspoon. The average American actually takes in 3,436 mg a day—more than double the recommendation.
Before you reassure yourself that you're in the clear because you banned the saltshaker from your table long ago, remember this: The biggest culprits by a long shot are processed and packaged foods. "The vast majority of salt [we consume], 80% or more, is already in processed and pre-prepared foods," says Marion Nestle, PhD, professor of nutrition at New York University.
Here's just how fast the salt can add up on a typical day:
Breakfast: 1 whole grain bagel (490 mg) with 2 Tbsp fat-free cream cheese (211 mg) and 6 oz yogurt (95 mg)
Snack: 2 Tbsp peanut butter (147 mg) on 6 wheat crackers (194 mg)
Lunch: Sandwich with 2 slices low-salt turkey (432 mg), 1 slice American cheese (266 mg), and 2 tsp mustard (114 mg) in a flour tortilla (490 mg) with 1 dill pickle spear (306 mg) and 1c vegetable soup (960 mg)
Snack: 1 wheat pita (340 mg) with 2 Tbsp hummus (114 mg)
Dinner: ½ c pasta (4 mg) with ½ c jarred tomato sauce (480 mg) and 2 meatballs (232 mg), 1 slice garlic bread (400 mg), and salad with reduced fat ranch dressing (336 mg)
Dessert: Homemade apple crisp (495 mg) with ½ c vanilla ice cream (53 mg) and 2 Tbsp caramel sauce (60 mg)
The grand total: 6,219 mg, more than quadruple the daily recommended amount.
So even if you've never touched a McNugget, you can still quickly eat more sodium than you should. And all that salt doesn't only hurt your heart and your waistline. An emerging body of research has also linked excessive sodium intake to cancer, osteoporosis, diabetes, dementia, sleep apnea, and kidney disease
Scary stuff. Especially when you consider how hard it is to kick a salt habit. Your body only needs about 500 mg of sodium a day to maintain the right balance of fluids, transmit nerve impulses, and move your muscles. When you eat far more than that—as most of us do—it alters your brain chemistry. Research shows that salt triggers the release of the feel-good neurotransmitter dopamine, which can make salty foods as addictive as nicotine and alcohol. And just like with any addiction, eating high-sodium foods makes your body crave more. In other words, the more Chicken McNuggets you have, the more you crave them. No matter what the cost.
More from Prevention: 6 Ways To Ease A Food Addiction
More From Healthy Eating Tips
These Are the 15 Absolute Best Protein Powders
Which Is Healthier: Chicken or Tofu?
10 Breakfasts Recipes That Boost Your Brain
10 Green Juice Recipes That Actually Taste Great
50 Foods the World's Healthiest Women Eat Daily
22 Metabolism-Boosting Foods
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15 Everyday Foods That Boost Your Immune System
20 Ways To Slim Down Your Sandwich
Is A Vitamin Deficiency Making You Fat?
Are You A Junk Food Addict?
Are You A Shopping Addict?
Do You Buy The Food Addiction Excuse?
15 High-Sodium Foods To Avoid
16 Surprising Foods Packed With Sodium
Are You Addicted To Exercise?

redpajama_set_name: RedPajamaCommonCrawl


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Rinjani trekking – scrambling to the summit - Oliwia Papatanasis | The Ollie
Active Indonesia Lombok
Tags: mountains, volcano
Rinjani trekking – scrambling to the summit
The way to the summit of Rinjani
That night I woke up at two o'clock. As we were warned the first part was murderous. Very steep, dusty. Groups hiking next to each other formed huge clouds of dust, there was no air to breathe. People were sliding two meters down, often falling, you had to pay attention not only to yourself but to others. It was the only part when we were able to go together in the group. When we got the ridge of the volcano it was quieter for a while. A moment to take breath and then further hiking up. It was dark and more and more cold. Despite being on the move it was easy to cool down duo to a fatigue and very strong wind. It would be hard without headlamp. It was better not to look sideways, because the path was narrow. We had to support one of our teammate, she got so nervous about a false step and fall into the abyss, two times she did not want to go any further. In the dark imagination can play tricks. Indeed it was steep, you could fall down, but you would need a bit more than one false step. Once we descended from the summit she said that in the day light it looks completely different.
The way seemed very lengthy, we had to carefully put each step. From a certain point we no longer waited for each other, everyone had to start a fight with himself. The last approach was like the beginning. People moaned, fell on each other, slid down and straggled. Those who still have some energy joked that they will cry if they reach the summit. It was easy to connect with strangers in pain;) My trekking poles went into motion. Despite their help still after each step I slide down, the fine volcanic dust was ankle-deep. It seemed that it would never end. I was additionally burden by the sneakers, I had inside – as I later laughed – half Rinjani volcano; D. Well, it's good that from the childhood I hang around barefoot, otherwise I would not have survived. I felt every stone on the way, and during the descent fingers hit every rock.
In addition, I did a trick to myself. At first it seemed to me that on a distant summit I already see the lights of the headlamps and flashlights of the first conquerors. It is likely that indeed it was the case. So then I looked at these lights in front of me and I told myself that a little more and I get there. To my disappointment, the time passed, and the brightest light was very far away. I started to fight in order not to surrender. After some time, someone suddenly started talking to me in the dark, that he realized that it seems to him that this torch on the top is a star and he asked me whether it is really not a star. And bam, revelation! : D All the time I was chasing a star. Even though we still had some energy to laugh and so we moved on.
The very summit peak was not easy to reach, you go through a mini maze, sliding down along the walls. The fatigue did not allow to enjoy the success at the beginning. I took a seat among the friends and we waited for the sunrise. It got terribly cold, apparently something around 4 degrees Celsius. There was a strong and icy wind. People began to "sing" something like "sun, damn, come oooon, come on, plizzzz". I was shaking like a leaf despite trekking trousers and leggings underneath, T-shirts, sweatshirts and jacket, scarves and hood on the head. Gloves I put on already during hiking because I was not able to keep the poles in frozen hands. I missed the hat I lost on the Kawah Ijen.
The sunrise and the crater of Rinjani volcano
The sun finally put first beams over the clouds. The scene was beautiful, both sunrise and the crater, which we could see from the top in all its glory, in addition painted with the rays of the rising sun. Cold quickly convinced part of the tourists to leave and people could peacefully shoot photos and movies of the views and themselves.
The descent was not easy, but you could slip down to move faster. Knees barely gave any response, but luckily I had two supports. After reaching the tents, eating a lunch, we cleaned a little bit ourselves from the dust and we went down. It was however the least pleasant part of the trek. The route was steep and covered with gravel. You could not do a single "normal" step, each one ended up slipping. Once even I landed on my butt. Leg muscles were confused not knowing what was happening. I have also seen the spectacular collapse of a porter (there was a myth that such things do not happen). It reminded a crash test aircraft in the desert, which I saw on TV. Fortunately there was no injuries and I was not caught along the way, missed by about a meter. He flew about eight meters down and loosing his flip-flops.
After a long journey we arrived to the end of the trekking in Sembilan. A colleague from Malaysia despite the lack of strength was very brave and reached the top. We supported her, and were glad when she did it, unfortunately, it heavily delayed our trekking. Descent also took her a lot more time and finally we had to revise our further plans. It was so late that there was no chance of getting to Bali, so we decided to stop after three days of hardcore in a nice and small hotel in Senggigi to rest. The stomachs had to recover after accidental drinking of a puddle water (we grabbed the wrong one, instead of drinking water we got the dish washing water). We saw the sand a few swallows later. In a dark tent we could not see that the water in the bottle is yellowish;)). At the end of the trek we took a ride to pick up the backpacks and then another ride to the hotel in Senggigi.
Summary: IT WAS GREAT AND I RECOMMEND TO EVERYONE!
Gunung Rinjani
Gunung Rinjani, an active volcano on the island of Lombok in Indonesia; belongs to stratovolcanos.
Geographic coordinates 8 ° 25'S 116 ° 28'E. The height of 3726 m above the sea level
Oval crater measuring 6 x 8.5 km in the west of the crater 230 m deep Segara Anak lake.
The first recorded eruption in 1847., the last in 2015.
The best travel photographs of 2015
List of my dreams, known as a bucket list
Oliwia 10 June 2017
What is the cheapest way to climb Kinabalu?
Oliwia 7 October 2015
Kinabalu – mysterious summit of Borneo
Travelling: LIGHT & HARD
Oliwia 26 July 2015


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Carmel-by-the-Sea is the city known for its surf spot Carmel Beach up to the Carmel River State Beach for the scuba lovers. It was located on the Monterey Peninsula with its museums and library of the historic Carmel Mission, and the fairytale cottages and galleries of its village-like center.
The Point Lobos State Natural Reserve is the top spot for lovers of the sea animals and whaling seeing.


The Inauguration of PRESIDENT BARACK OBAMA! The After Party!
"WHAT DOES THIS MOMENT IN HISTORY MEAN TO YOU?" We have a COMPLETE Inauguration wrap-up of The 44th President of The United States of America and the 1st African American President. We will have all the details: His speech. Her dress. Their first dance. The crowds. And an EXCLUSIVE EYEWITNESS ACCOUNT from DJ TRUCK(Mr. Leonard Cummings). The celebrities. The parties. What does this moment in History mean to you? Give us a call at 1-347-215-7727 and let us know.


Master Prod is the oldest company producing aluminum and PVC joinery in Bistrita.
Master Prod SRL is a 100% Romanian company with over 20 years of experience, specialized in the production and installation of aluminum and PVC carpentry with thermal insulating glass, horizontal and vertical closings, fixed or mobile, of spaces, interior compartments with fixed or sliding glass panels, as well as in the implementation of public and private rehabilitation projects.
We offer top services and products in the markets we operate: Romania, France, Belgium, Italy, Norway.
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Home » Join us for SportMedBC's 2019 AGM on Tuesday October 29th
Join us for SportMedBC's 2019 AGM on Tuesday October 29th
SportMedBC is inviting sport medicine professionals and fellow provincial sport sector partners to our upcoming Annual General Meeting on Tuesday, October 29 at Fortius Sport & Health in Burnaby, BC.
We have an exciting evening planned including the unveiling of SportMedBC's four-year strategic plan. We will also be introducing two new honorary members. The night will conclude with a special guest interview of a Canadian Olympic athlete.
Honorary Members are those individuals who have made outstanding contributions to the association over a number of years, and who have been selected and named as Honorary Members by the membership at a General Meeting.
Lynda Cannell was the President & CEO of SportMedBC from its inception in 1982 to 2012. Under Lynda's guidance and leadership, SportMedBC established many of the same programs and services that we continue to deliver to this day. Her commitment and dedication helped propel SportMedBC to new levels of professionalism and scope through its community programming and creative partnerships within the sport medicine community in British Columbia. We are thrilled to recognize Lynda for her contributions to SportMedBC by making her an honorary member.
Clyde Smith is a Sports Physiotherapist and was a founding principal of the British Columbia Sports Medicine Centre, which later was re-named The Allan McGavin Sports Medicine Centre.
Often regarded as a pioneer in sport therapy, Mr. Smith was one of the first people hired by Toronto's Sheridan College in 1973 to assist in the creation of a course designed to train professional sports team trainers. He was also instrumental in the early years of SportMedBC and later served on SportMedBC's Board of Directors from 2003 until 2011.
We are also pleased to welcome Canadian Olympic athlete Natasha Wodak as our special guest for the evening. Natasha was the winner of the 2019 Vancouver Sun Run, won gold while setting a Pan Am record in the women's 10,000M race at the 2019 Pan Am Games, and recently competed for Canada at the 2019 IAAF World Athletics Championships in Doha this past month. Natasha will be interviewed by Tom Meyenknecht – Chair for the BC Sports Hall of Fame and a radio host on TEAM 1040 in Vancouver.
As SportMedBC continues to promote awareness and leadership in sport medicine, sport science, sport training and health promotion we hope you can join us for what is sure to be a very special evening. Please use the registration link here to confirm your attendance.
SportMedBC team
Some Important Information
Five Director terms expire at the October 29th AGM. If you are interested in being nominated for a position on the SportMedBC Board, please click on the Nomination Information link below.
Nomination Information
Special Resolutions
Constitution and Bylaw Amendments
SportMedBC is hereby notifying members of the proposed Constitution and Bylaw Amendments that will be voted on at the upcoming AGM. You can review the document outlining the proposed changes here.
SportMedBC Elevation 2024 Strategic Plan
SportMedBC will also be seeking approval for our 2020-2024 Strategic Plan
Members are invited to review the proposed amendments above and raise any questions or provide comments prior to the annual meeting. Questions can be directed to ExecutiveDirector@sportmedbc.com.
Notice of Motions: Conferral of Honorary Membership Votes
AGM Agenda
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Whether you want to see or be scene, you gotta get out there. Just remember - there's always someone watching.
Matt & Kim rock FM94/9's Sandy Parts Festival (SPF).
Lil Jon headlines Intervention Sunday.
Lil Jon goes crunk wild at Five/Ten after spinning Intervention Sunday.
Almost closing time after The Small Black (an indie band from Brooklyn) show.
Australian DJ Feenixpawl plus LED wall equals another crushing club night.


Baseball in Victoria was anaemic in the first years of the new century and very nearly died out altogether, with only a few teams still playing until a comeback in 1904 when 6 teams competed. The MCC were back in the picture, W.G. Ingleton was back in Baseball and we included our first big import - Dr H. I. Twiss who bought with him a good knowledge of the game and was probably the first pitcher in Australia to introduce the curve ball.
"The Baseball season ended on Saturday, and MCC won the premiership, East Melbourne being second, and South Melbourne third. The season has been marked by an increase of interest in the game, not only by the players, but by the public. There have been six teams playing in the competition, and there is talk of this number being almost doubled next season. The game affords splendid winter exercise for cricketers, and its quickness and rapid changes make it an interesting game to watch. The game has never been in a stronger position in Victoria than at the present moment, and the efforts of the Victorian Baseball League to establish it firmly have been rewarded by a considerable amount of success."
"M.C.C played South Melbourne on the Melbourne Cricket Ground prior to the football match between Collingwood and Fitzroy, South Melbourne began with three runs in their first innings, but MCC scored seven at their first attempt. After this the game was very even, and the MCC won by 11 to 8, the Southerners having rather the advantage in the latter stages. For the winners, Twiss (until the fourth innings) and Quarterman pitched well, and Over was in good form as catcher, Ingleton on second base, was good and played well. Twiss hit a home run."


The new joint venture aims to provide a stable supply of competitive batteries to multiple automakers.
Toyota Motor Corporation and Panasonic Corporation announced Tuesday that the two companies will establish a joint venture next year to produce prismatic lithium-ion batteries, solid-state batteries, and next-generation batteries for electric vehicles.
Contracts concluded today confirm earlier reports of a formal partnership between the two companies, and build on an agreement that the pair announced in late 2017 to explore developing batteries with higher energy density in a prismatic cell arrangement.
The new joint venture aims to provide a stable supply of competitive batteries to multiple automakers — sold principally through Panasonic — as the EV market grows to meet evolving consumer needs and to address societal issues related to energy and climate change.
"As vehicle electrification accelerates toward the solving of such environmental issues, batteries are a most important element," Toyota and Panasonic said in a joint statement.
The joint venture seeks to address these issues by drawing on both companies' resources and expertise. Toyota will bring to the table its EV market data, manufacturing experience and advanced technologies related to solid-state batteries, while Panasonic will contribute its ability to make safe, high-capacity and high-output batteries at scale, as well as a customer base in Japan and abroad.
Equity participation in the joint venture will be split 51 percent for Toyota and 49 percent for Panasonic. Pending approval from competition law authorities, the partnership will officially launch by the end of 2020.
Toyota, which has been slower than other automakers to embrace EVs , announced in 2017 that it aims to sell more than 10 battery-electric models by the mid-2020s, contributing to sales of 5.5 million electrified vehicles by 2030. The new joint venture will help to support that effort.
Today's announcement also reflects the growing competition among battery manufacturers, according to Mitalee Gupta, energy storage analyst at Wood Mackenzie Power & Renewables. While Panasonic has been a Tier 1 lithium-ion battery cell supplier for several years, she noted that Chinese vendors such as CATL and BYD have been ramping up their battery cell production and gaining market share.
"This strategic partnership shows that Panasonic is now looking beyond Tesla to keep its place in the race to capture the global EV market, by leveraging Toyota's position as one of the biggest automakers," said Gupta.
Tesla, one of Panasonic's highest-profile customers, announced recently that it is looking at other cell suppliers for its Shanghai Gigafactory, including local Chinese companies. Reports this week show that Tesla has been in talks with China's Tianjin Lishen, but that no deal has been reached to date.
Around 60 percent of global cell manufacturing currently takes place in China, according to Gupta. South Korean vendors such as LG Chem and Samsung SDI are in the process of setting up manufacturing bases in the country to ensure that they aren't losing out on this global competition, she said. Toyota's joint venture with Panasonic will also include factories in China, as well as Japan.
Through the joint venture, Toyota and Panasonic have also committed to researching and developing solid-state batteries, a technology that according to Wood Mackenzie Power & Renewables will start becoming commercially viable after 2025. Last year WoodMac tracked more than half a billion dollars worth of investments in solid-state technologies from automakers and cell suppliers.
"With the EV industry collectively trying to focus on improving energy density of today's battery cells and overcoming current challenges with cobalt cathodes and graphite anodes, these investments are going to grow rapidly in the coming years," said Gupta.


Where can I find out about upcoming company results and economic announcements?
If you have access to the Internet, the CCN financial pages provides a regional diary of upcoming economic indicators. Otherwise, the inside front cover of Monday's Financial Review has a weekly diary of company results, announcements, company meetings and the release of economic data.


text:
(from Okayama, Japan 1896–1967) was a potter in Imbe, Japan.  He helped to establish the Japan Kōgei Association in 1955, and was deemed a living national treasure in 1956 for his work in the Bizen style ceramics.
He was a member of what is known as the "Momoyama revival movement" of the 1930s and is credited with having rediscovered the techniques used to produce the wabi teawares of the Azuchi-Momoyama period.

References

External links
 http://www.e-yakimono.net/html/momoyama-revival-jt.html

Japanese potters
People from Okayama Prefecture
1967 deaths
1896 births
Living National Treasures of Japan
20th-century ceramists
19th-century Japanese people
20th-century Japanese people

redpajama_set_name: RedPajamaWikipedia


The Marin Centre in French-speaking canton of Neuchâtel has more than 30 shops and numerous restaurants and cafes on two levels, providing a wide range of goods and services.
To supply the main entrance door, record was chosen. A huge and impressive record revolving entrance door some 6 meters in diameter is the first thing visitors to the Marin Centre see.


text:
The Uncanniness of Missionary Others
A Discursive Analysis of a Century of Anthropological Writings on Missionary Ethnographers
in Religion and Society
Travis Warren Cooper
Travis Warren CooperButler University twcooper@butler.edu
Search for other papers by Travis Warren Cooper in
https://doi.org/10.3167/arrs.2018.090106
anthropology of religion; Christian missions; colonialism; disciplinary for mation; ethnographic method; history of anthropology; travel
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This article examines discussions of missionaries penned by anthropologists and existing in disciplinary consciousness. Questions of alterity, distance and sameness, the potentially exploitative effects of ethnography, and the uncomfortable colonialist underpinnings of both missionary and anthropological pasts come to the fore in these texts. Drawing on a wealth of journal articles, ethnographic monographs, and edited volumes, I identify, describe, and analyze six predominant discourses on missionaries, including anthropological depictions of missionaries as foils (Discourse One), as intermediaries (Discourse Two), and as present in good or bad manifestations (Discourse Three). Other threads constitute missionaries as data (Discourse Four), conceive of them as methodological ancestors and ethnographic colleagues (Discourse Five), or identify them reflexively as both anthropologists and Christians (Discourse Six). I suggest that missionaries serve as an archetypical foil against which the anthropological discipline emerges. Missionary ethnographers are for anthropologists a necessarily uncanny, repressed, productive other.
TRAVIS WARREN COOPER holds a double PhD in Anthropology and Religious Studies and lectures at Butler University. His dissertation project, "The Digital Evangelicals: Contesting Authority and Authenticity after the New Media Turn," examines religious boundary maintenance strategies in the era of social media. His current research focuses on the various social architectures that structure everyday American life-worlds, rituals, and traditions—systems ranging from social media ideologies to evangelical material culture and the communicative environment. As an ethnographer of the American Midwest, he studies suburban habitudes, residential and religious architecture, and the anthropology of design. E-mail: twcooper@butler.edu
https://www.berghahnjournals.com/abstract/journals/religion-and-society/9/1/arrs090106.xml
Advances in Research
© Berghahn Books 2018
PORTRAIT: Eileen Barker
Interaction Rituals and Religious Culture in the Tea Party
Kosher Biotech
Secular Routes and Theological Drifts in Modern Anthropology
Special Section: Ritual and Critical Reflexivity. Edited by Emma Gobin
The Death Throes of Sacrificed Chicken
Assessing Ritual Experience in Contemporary Spiritualities
Dynamics of Ritual Reflexivity in the Alevi Cem of Istanbul
Assessing and Adapting Rituals That Reproduce a Collectivity
An Author Meets her Critics
Around Joan Wallach Scott's Sex and Secularism
Discourse One: The Missionary as Anthropological Foil
Discourse Two: The Missionary as Practical Intermediary
Discourse Three: Good Missionaries versus Bad Missionaries
Discourse Four: The Missionary as Data
Discourse Five: The Missionary as Methodological Ancestor and Alternative Ethnographer
Discourse Six: Christian Anthropologists and the Anthropology of Christianity
Toward a Provisional Conclusion
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Article by Travis Warren Cooper


Non Rotationally Symmetric (Freeform) optics are becoming increasingly widely used for both civil and military applications. Advanced manufacturing technologies such as Ultra Precision Raster Milling allow a wide variety of shapes to be created.
Popular applications include Printing / Scanning, Head-Up Displays and Ophthalmics. Taylor Hobson's market-leading Form Talysurf measurement system has long been the system of choice for 3D measurement of rotationally symmetric optics this technology is now meeting the challenge of Freeform Optics.


Go ahead, do yourself and your family a favor ... live longer.
Black Raspberries, affectionately known as "Blackcaps" by growers, are native to North America. These uniquely flavored berries are popular in specialty foods, especially jams and ice cream, and are used as a coloring agent as well. A majority of the crop is processed as individually quick frozen raspberries, frozen raspberry puree, or frozen raspberry juice concentrate. Other processing methods include canning, drying, processed bakery fruit fillings and essence, as well as aseptic packaging.
1. Oregon Raspberry and Blackberry Commission.


En una entrevista en de CNN, agreg: tuviera algn respeto por ella, probablemente habra sido hiriente la solicitud de un comentario al respecto, un portavoz del Departamento Tesoro, asegur: Mnuchin Golden Goose Sneakers estn reembolsando al gobierno por su viaje, y ella no recibe compensacin por los productos que menciona calific el desenlace como un resultado y dijo que estaba tratando de redirigir los nuevos seguidores que haba ganado en Instagram, por el ahora viral intercambio de mensajes, hacia un proyecto de recaudacin de fondos para un amigo que sufri un derrame cerebral.
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Day-late birthday shout-outs: Happy 72nd Ryan Steele! You look GOOD!
La Steele edited, cameoed and directed this doozie, and you should vote for it in Virgin Radio's 2011 Fake Film Festival, starring Jeff Lynch (heart) and Amy Goodmurphy.
Overall, I give this 60-second masterpiece two drunk punches to the face, which is a sign that Amy successfully channelled the spirit of Julia Roberts.