Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

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A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

The basic paradigm of asset pricing is in vibrant f lux. The purely rational approach is being subsumed by a broader approach based upon the psychology of investors. In this approach, security expected returns are determined by both risk and misvaluation. This survey sketches a framework for understanding decision biases, evaluates the a priori arguments and the capital market evidence bearing on the importance of investor psychology for security prices, and reviews recent models.

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Investor Psychology and Asset Pricing

An efficient method is developed for pricing American options on stochastic volatility/jumpdiffusion processes under systematic jump and volatility risk. The parameters implicit in deutsche mark (DM) options of the model and various submodels are estimated over the period 1984 to 1991 via nonlinear generalized least squares, and are tested for consistency with $/DM futures prices and the implicit volatility sample path. The stochastic volatility submodel cannot explain the "volatility smile" evidence of implicit excess kurtosis, except under parameters implausible given the time series properties of implicit volatilities. Jump fears can explain the smile, and are consistent with one 8 percent DM appreciation "outlier" observed over the period 1984 to 1991. The central empirical issues in option pricing are what distributional hypotheses are consistent with observed option prices, and whether those distributional hypotheses are consistent with the properties of the un

/pdf/jumps-and-stochastic-volatility-exchange-rate-processes-1yfupj7xl1.pdf

Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options

Matrix Differential Calculus with Applications in Statistics and Econometrics

This paper studies the role of f luctuations in the aggregate consumption‐wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these f luctuations in the consumption‐wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption‐wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption‐aggregate wealth ~human capital plus asset holdings! ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents’ expectations of future returns on the market portfolio. UNDERSTANDING THE EMPIRICAL LINKAGES between macroeconomic variables and financial markets has long been a goal of financial economics. One reason

/pdf/consumption-aggregate-wealth-and-expected-stock-returns-19ub23j0ab.pdf

Consumption, Aggregate Wealth, and Expected Stock Returns

As a centralized, computerized, limit order market, the Paris Bourse is particularly appropriate for studying the interaction between the order book and order flow. Descriptive methods capture the richness of the data and distinctive aspects of the market structure. Order flow is concentrated near the quote, while the depth of the book is somewhat larger at nearby valuations. We analyze the supply and demand of liquidity. For example, thin books elicit orders and thick books result in trades. To gain price and time priority, investors quickly place orders within the quotes when the depth at the quotes or the spread is large. Consistent with information effects, downward (upward) shifts in both bid and ask quotes occur after large sales (purchases). MANY OF THE WORLD'S major stock exchanges, such as the New York and Tokyo Stock Exchanges, rely at least partially upon limit orders for the provision of liquidity. Therefore, it is important to understand the placement of limit orders and their contribution to liquidity and price formation. There is a complex relationship between investor order strategies and the short-term dynamics of asset prices, the transmission of information in security markets, the costliness of trading, and the nature of the liquidity available from the market. We are interested in understanding the intertwined dynamics of the order flow and order book: how the order flow reacts to the state of the order book and informational events in the marketplace, mechanically updates the

https://www.jstor.org/stable/pdfplus/2329330.pdf

An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse

Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting ('data snooping'). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power. The failure to detect out-of-sample predictability is not due to lack of power.

/pdf/implementing-statistical-criteria-to-select-return-3qgp8uequ9.pdf

Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?

Before the opening of the Paris Bourse, traders place orders and indicative prices are set. This offers a laboratory to study empirically the tâtonnement process through which markets discover equilibrium prices. Since preopening orders can be revised or canceled before the opening, indicative prices could be noise. We test this against the hypothesis that preopening prices reflect learning. Early in the preopening the noise hypothesis is not rejected. As the opening gets closer, the informational content and efficiency of prices increase and the learning hypothesis is not rejected. We also propose a GMM‐based estimate of the speed of learning.

Price Discovery and Learning during the Preopening Period in the Paris Bourse

We analyze the introduction of a nonredundant option, which completes the markets, and the effects of this on information revelation and risk sharing. The option alters the interaction between liquidity and insider trading. We find that the option mitigates the market breakdown problem created by the combination of market incompleteness and asymmetric information. The introduction of the option has ambiguous consequences on the informational efficiency of the market. One the one hand, by avoiding market breakdown, it enables trades to occur and convey information. On the other hand, the introduction of the option enlarges the set of trading strategies the insider can follow. This can make it more difficult for the market makers to interpret the information content of trades and consequently can reduce the informational efficiency of the market. The introduction of the option also has an ambiguous effect on the profitability of insider trades, which can either increase or decrease depending on parameter values. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Pierre Hillion

Papers

An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse

Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?

Price Discovery and Learning during the Preopening Period in the Paris Bourse

Insider and Liquidity Trading in Stock and Options Markets

The manipulation of closing prices