Optimal algorithmic trading and market microstructure

The efficient frontier is a core concept in Modern Portfolio Theory. Based on this idea, we will construct optimal trading curves for different types of portfolios. These curves correspond to the algorithmic trading strategies that minimize the expected transaction costs, i.e. the joint effect of market impact and market risk. We will study five portfolio trading strategies. For the first three (single-asset, general multi-asseet and balanced portfolios) we will assume that the underlyings follow a Gaussian diffusion, whereas for the last two portfolios we will suppose that there exists a combination of assets such that the corresponding portfolio follows a mean-reverting dynamics. The optimal trading curves can be computed by solving an N-dimensional optimization problem, where N is the (pre-determined) number of trading times. We will solve the recursive algorithm using the "shooting method", a numerical technique for differential equations. This method has the advantage that its corresponding equation is always one-dimensional regardless of the number of trading times N. This novel approach could be appealing for high-frequency traders and electronic brokers.quantitative finance; optimal trading; algorithmic trading; systematic trading; market microstructure

Testing the statistical significance of sector & regional diversification

The question as to whether it is better to diversify a real estate portfolio within a property type across the regions or within a region across the property types is one of continuing interest for academics and practitioners alike. The current study, however, is somewhat different from the usual sector/regional analysis taking account of the fact that holdings in the UK real estate market are heavily concentrated in a single region, London. As a result this study is designed to investigate whether a real estate fund manager can obtain a statistically significant improvement in risk/return performance from extending out of a London based portfolio into firstly the rest of the South East of England and then into the remainder of the UK, or whether the manger would be better off staying within London and diversifying across the various property types. The results indicating that staying within London and diversifying across the various property types may offer performance comparable with regional diversification, although this conclusion largely depends on the time period and the fund manager’s ability to diversify efficiently

Investor Sentiment in Japanese and U.S. Daily Mutual Fund Flows

We find evidence that is consistent with the hypothesis that daily mutual fund flows may be instruments for investor sentiment about the stock market. We use this finding to construct a new index of investor sentiment, and validate this index using data from both the United States and Japan. In both markets exposure to this factor is priced, and in the Japanese case, we document evidence of negative correlations between Bull' and Bear' domestic funds. The flows to bear foreign funds in Japan display some evidence of negative correlation to domestic and foreign equity funds, suggesting that there is a foreign vs. domestic sentiment factor in Japan that does not appear in the contemporaneous U.S. data. By contrast, U.S. mutual fund investors appear to regard domestic and foreign equity mutual funds as economic substitutes.

A machine learning algorithm applied to macroeconomic factor investing

This paper examines the extent to which macroeconomic indicators can be used to determine the optimal allocation of an extended Fama French 5-Factor model which includes the risk-free rate. The study is based on Modern Portfolio Theory (MPT) as developed by Markowitz(1952) and Smart Beta Investing. The algorithm combines MPT with two Machine Learning (ML) Algorithms (K-means Clustering and Random Forest) to predict the macroeconomic state and arrive at the according optimal ‘tactical’ portfolio allocation of each security over the investment period. The research contributes to the existing literature of ML Algorithm performance applied to Smart Beta macroeconomic strategies

Portfolio diversification and internalization of production externalities through majority voting

In absence of markets for externalities, the authors look for governances and conditions under which majority voting among shareholders is likely to give rise to efficient internalization. The central and natural role played by a governance of stakeholders is underlined and benchmarked.Production externalities; majority voting; portfolio diversification; general equilibrium; stakeholder governance; mean voter

Asset Clusters and Asset Networks in Financial Risk Management and Portfolio Optimization

In this work we use explorative statistical and data mining methods for financial applications like risk management, portfolio optimization and market analysis. The outcomes are visualized and the relations are quantified by mathematical measures. Researchers, analysts and decision makers can visually explore the structures and can carry out management initiatives based on automatic measures provided by the system. There are example applications to equity and loan portfolios

Red, Yellow, and Green: A Taxonomy of 401(k) Portfolio Choices

One measure of financial literacy is the quality of portfolio decision-making in 401(k) plans. Applying a qualitative framework to a dataset of nearly three million 401(k) accounts, we estimate that 43% construct “green” portfolios with balanced exposure to diversified equities, while 26% construct “yellow” portfolios with possibly too-aggressive or too-conservative equity holdings. Another three in ten participants make egregious errors and have “red” portfolios— either holding zero in equities or over concentrating their account in employer stock. Using a subset of our sample, we estimate the costs of portfolio errors (and the potential gain from improved allocations) at roughly 60 to 350 basis points in expected real return per year, depending on the initial portfolio held. Low income, low wealth and female participants are more likely to experience the largest gains from better portfolios, given their tendency to hold less aggressive portfolios