Long-Horizon Stock Returns Are Positively Skewed
Adam Farago and Erik Hjalmarsson (University of Gothenburg)
April 28, 2021
At long horizons, multiplicative compounding induces strong-to-extreme positive skewness into stock returns; the magnitude of the effect is primarily determined by single-period volatility. Consequently, at horizons greater than five years, returns –individual or portfolio– will be positively skewed under reasonable parametrizations. From an investor perspective, the strong positive skewness implies that the mean compound return will serve as a poor guide for typical long-horizon outcomes. Moreover, the large effects of compounding on higher-order moments are shown to affect the validity of Taylor expansions used to approximate preferences for skewness, when applied to returns of annual or longer horizons.

Long-run expected stock returns
Paul Geertsema and Helen Lu (University of Auckland)
May 11, 2021
We predict individual stock returns over horizons from 1 month to 10 years using machine learning. Cumulative stock returns are significantly predictable in the cross-section over all horizons. One-month ahead predicted returns explain only a quarter of the variation in 10-year predicted returns, suggesting that predicted returns at different horizons follow distinct dynamics. Predictors related to turnover and volatility are influential at all horizons. Momentum, cash flow and size related predictors are mostly important at shorter horizons, while dividend yield, value and long-term reversal related predictors are more important at longer horizons.

Investor Sentiment, Media and Stock Returns: The Advancement of Social Media
Ioanna Lachana and David Schröder (University of London)
May 8, 2021
Recent research in behavioural finance has shown the importance of investor sentiment to explain stock market returns. In light of the changes in the media scene from traditional print media towards social media platforms over the past decade, this paper analyses and compares the effect of sentiment indices obtained from different media sources in their ability to explain stock market movements. Using a large set of articles and reader comments covering the time from 2006 to 2020, we show that social media is better in capturing investor sentiment than traditional media outlets. The results of the paper highlight the importance of alternative media sources to understand market behaviour.

Time-varying Impact of Investor Sentiment
Pengfei Sui (Chinese University of Hong Kong)
February 1, 2021
I present a dynamic equilibrium model of investor sentiment in which investors form beliefs by overly extrapolating past returns. The key contribution of my model is to connect mispricing with investor sentiment through the market impact of extrapolators, and to provide novel insights into the predictability of market returns. When their wealth level is high, extrapolators drive the asset prices. In this case, their high investor sentiment makes the current asset price overvalued, and the future asset price will decline because high investor sentiment will cool down over time. Therefore, investor sentiment negatively predicts future market returns. When extrapolators’ wealth level is low, high investor sentiment predicts high future returns since the market is under a price correction. I find strong support for my model in the data. My model also matches investor sentiment in surveys, and it captures many documented patterns of boom-bust cycles in the stock market.

What Moves the Market? Individual Firms’ Earnings Announcements Versus Macro Releases As Drivers of Index Returns
Maria Ogneva (U. of Southern California) and Jingjing Xia (City U. of Hong Kong)
April 30, 2021
In this paper, we characterize the relative importance of two sources of fundamental market-wide news—large firms’ earnings announcements and macroeconomic releases. Our investigation is motivated by growing concerns in the financial community about the increasing impact of individual firms’ news on the broad stock market indices and the disconnect between the stock market and the economy at large. We leverage the S&P500 index futures data and use narrow intraday and overnight windows to isolate the market-wide reactions to earnings and macro announcements. We find that earnings announcements represent an economically significant source of index-level market activity—an average earnings announcement experiences around 21% (47%) of abnormal volatility (trading volume) associated with an average macroeconomic release. The returns earned over earnings announcement windows serve as a significant driver of daily index price movement. Importantly, earnings announcements’ contribution to index-level volatility has been relatively stable over our sample period from 2004 to 2018, while we observe a drastic decrease in the volatility explained by macro announcements. The latter is consistent with a growing disconnect between the stock market and the broader macroeconomy.

Bitcoin Sentiment Index and Stock Market Return
Najma Ali Soomro (Sukkur IBA University), et al.
May 6, 2021
The returns predictions and price movements of financial markets are predicted through online search engines. These search engines claim to trade sentiments of individual investors. This study aims to determine the changes in the American stock market returns due to Bitcoin investors’ sentiments. The Bitcoin sentiment index is constructed and used as a benchmark for Bitcoin investors’ sentiments from the time period of 2013-2018. This index is constructed through searching terms from top business magazines and journals available online. Such index is used as benchmark to determine the bitcoin potential investor sentiments and their impact on S&P returns. By using the ordinary least square method it was found that there is a negative impact of BSI on S&P returns. Further vector autoregressive(VAR) model is used to determine the association between these economic time series. According to VAR results it was found positive significant impact of S&P returns on BSI whereas, BSI itself was unable to predict S&P returns. Therefore, it can be said that S&P returns causes BSI.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno