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Expectations of Returns and Expected Returns
We analyze time series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.
Journal Article
DEPRESSION BABIES: DO MACROECONOMIC EXPERIENCES AFFECT RISK TAKING?
We investigate whether individual experiences of macroeconomic shocks affect financial risk taking, as often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1960 to 2007, we find that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk, are less likely to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are more pessimistic about future stock returns. Those who have experienced low bond returns are less likely to own bonds. Results are estimated controlling for age, year effects, and household characteristics. More recent return experiences have stronger effects, particularly on younger people.
Journal Article
Deep Learning for Stock Market Prediction
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
Journal Article
Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the early 1990s to the present, we find striking evidence of divergent behaviour in the dynamics of return spillovers vs. volatility spillovers: return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts.
Journal Article
The Media and the Diffusion of Information in Financial Markets: Evidence from Newspaper Strikes
The media are increasingly recognized as key players in financial markets. I investigate their causal impact on trading and price formation by examining national newspaper strikes in several countries. Trading volume falls 12% on strike days. The dispersion of stock returns and their intraday volatility are reduced by 7%, while aggregate returns are unaffected. Moreover, analysis of return predictability indicates that newspapers propagate news from the previous day. These findings demonstrate that the media contribute to the efficiency of the stock market by improving the dissemination of information among investors and its incorporation into stock prices.
Journal Article
Measuring Uncertainty
This paper exploits a data rich environment to provide direct econometric estimates of time-varying macroeconomic uncertainty. Our estimates display significant independent variations from popular uncertainty proxies, suggesting that much of the variation in the proxies is not driven by uncertainty. Quantitatively important uncertainty episodes appear far more infrequently than indicated by popular uncertainty proxies, but when they do occur, they are larger, more persistent, and are more correlated with real activity. Our estimates provide a benchmark to evaluate theories for which uncertainty shocks play a wie in business cycles.
Journal Article
On the Timing and Pricing of Dividends
We present evidence on the term structure of the equity premium. We recover prices of dividend strips, which are short-term assets that pay dividends on the stock index every period up to period T and nothing thereafter. It is short-term relative to the index because the index pays dividends in perpetuity. We find that expected returns, Sharpe ratios, and volatilities on short-term assets are higher than on the index, while their CAPM betas are below one. Short-term assets are more volatile than their realizations, leading to excess volatility and return predictability. Our findings are inconsistent with many leading theories. [PUBLICATION ABSTRACT]
Journal Article
Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?
Why is the equity premium so high, and why are stocks so volatile? Why are stock returns in excess of government bill rates predictable? This paper proposes an answer to these questions based on a time-varying probability of a consumption disaster. In the model, aggregate consumption follows a normal distribution with low volatility most of the time, but with some probability of a consumption realization far out in the left tail. The possibility of this poor outcome substantially increases the equity premium, while time-variation in the probability of this outcome drives high stock market volatility and excess return predictability.
Journal Article