State-dependent Asset Allocation Using Neural Networks

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods

Liquidity Costs, Idiosyncratic Volatility and Expected Stock Returns

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-based returns and then estimate IV. We show that there is a premium for IV in value-weighted portfolios, but this premium is less strong after correcting returns for microstructure bias. We further show that this premium is driven by liquidity in the prior month after correcting returns for microstructure noise. The pricing results from equally-weighted portfolios indicate that IV does not predict returns either before or after controlling for liquidity costs. These findings are robust after controlling for common risk factors as well as analysing double-sorted portfolios based on IV and liquidity

Institutional ownership and liquidity commonality: evidence from Australia

We study the liquidity commonality impact of local and foreign institutional investment in the Australian equity market in the cross-section and over time. We find that commonality in liquidity is higher for large stocks compared to small stocks in the cross-section of stocks, and the spread between the two has increased over the past two decades. We show that this divergence can be explained by foreign institutional ownership. This finding suggests that foreign institutional investment contributes to an increase in the exposure of large stocks to unexpected liquidity events in the local market. We find a positive association between foreign institutional ownership and commonality in liquidity across all stocks, particularly in large and mid-cap stocks. Correlated trading by foreign institutions explains this association. However, local institutional ownership is positively related to the commonality in liquidity for large-cap stocks only

Insiders\u27 Profits in the Australian Equities Market

In this paper we investigate if directors of Australian companies earn persistent profits on their reported trades, if these abnormal profits are significant enough to be mimicked by outsiders, and if these insider trades have an effect on returns of other investors. We find that insiders take advantage of their private information in stocks of larger corporations, but generally do not in medium and small capitalization firms, indicating that they insiders are attracted to the liquidity and a greater presence of uninformed traders in large stocks. Insiders appear able to determine the value of their information in by trading larger volume and larger portion of their holdings when they have access to valuable information. We find that outsiders can make profitable trades by following insider\u27s trades in large firms, but abnormal returns mimicking insiders in small and medium size firms are limited to insiders\u27 sell trades only, and otherwise result in losses for outsiders. Implications on market fairness and integrity are discussed and conclude that market quality can be improved with public access to good quality aggregated data on reported director insider trades

State-dependent asset allocation using neural networks

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods

Insiders’ profits in the Australian Equities Market

In this paper we investigate if directors of Australian companies earn persistent profits on their reported trades, if these abnormal profits are significant enough to be mimicked by outsiders, and if these insider trades have an effect on returns of other investors. We find that insiders take advantage of their private information in stocks of larger corporations, but generally do not in medium and small capitalization firms, indicating that they insiders are attracted to the liquidity and a greater presence of uninformed traders in large stocks. Insiders appear able to determine the value of their information in by trading larger volume and larger portion of their holdings when they have access to valuable information. We find that outsiders can make profitable trades by following insider’s trades in large firms, but abnormal returns mimicking insiders in small and medium size firms are limited to insiders’ sell trades only, and otherwise result in losses for outsiders. Implications on market fairness and integrity are discussed and conclude that market quality can be improved with public access to good quality aggregated data on reported director insider trade

Do individual investors demand or provide liquidity? New evidence from dividend announcements

[[abstract]]This paper provides new evidence related to whether individual investors demand or provide liquidity. While net trading is often used in the literature, it is improper in our research since buying and selling by individual investors increase by almost the same amount around dividend announcements. By differentiating buying and selling, we find that individual buyers demand liquidity while individual sellers provide liquidity around dividend announcements. Specifically, the buying volume of individual investors before and during dividend announcements negatively predicts future returns, while it is positively associated with past and contemporaneous returns. The selling volume of individual investors shows a similar relationship with returns.[[notice]]補正完