Does Active Portfolio Management Create Value? An Evaluation of Fund Managers' Decisions

In this paper, I obtain new measures of the value of active portfolio management by forming replicating portfolios. These measures allow for a separate evaluation of fund managers' strategic and tactical decisions. I also obtain new evidence on the value of trading by decomposing it into long-term trading decisions, short-term trading decisions, and trading that is the result of regulatory restrictions. Overall, the evidence supports the value of active portfolio management and that the average fund manager creates value for its investors. Moreover, the results show a positive relation between the value created and trading activity.Mutual Funds; Portfolio Evaluation; Performance Attribution; Trading

Which Past Returns Affect Trading Volume?

Anecdotal evidence and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this prediction using linear panel regressions as well as negative binomial panel regressions and Logit panel regressions. We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the probability to trade stocks in a given month) and are thus able to confirm predictions of overconfidence models. However, contrary to intuition, the effect of market returns on subsequent trading volume is stronger for the whole group of investors. Using survey data of our investor sample, we present evidence that individual investors, on average, are unable to give a correct estimate of their own past realized stock portfolio performance. The correlation between return estimates and past realized returns is insignificant. For the subgroup of respondents, we are able to analyze the link between the ability to correctly estimate the past realized stock portfolio performance on the one hand and the dependence of trading volume on past returns on the other hand. We find that for the subgroup of investors that is better able to estimate the own past realized stock portfolio performance, the effect of past portfolio returns on trading volume is stronger. We argue that this finding might explain our results concerning the relation between past returns and subsequent trading volume.Individual investors; Investor behavior; Trading volume; Stock returns and Trading Volume; Overconfidence; Discount broker; Online broker; Online banks; Panel data; Count data

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

Stock portfolio structure of individual investors infers future trading behavior

Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade in a similar way. With data from the central register of shareholdings in Sweden, we model the market in a similarity network, by considering investors as nodes, connected with links representing portfolio similarity. From the network, we find groups of investors that not only identify different investment strategies, but also represent groups of individual investors trading in a similar way. These findings suggest that the stock portfolios of investors hold meaningful information, which could be used to earn a better understanding of stock market dynamics.Comment: 9 pages, 4 figures, 1 tabl

Optimal portfolio control with trading strategies of finite variation

We propose a method for portfolio selection where the trading strategies are constrained to have a finite variation. A simulation example shows a significant reduction in trasaction costs as compared to log-optimal portfolio, for almost same final wealth

Portfolio selection with time constraints and a rational explanation of insufficient diversification and excessive trading

Private investors have limited time available for learning about stocks as they need to divide their time between stock analysis and work. This paper analyzes the influence of learning constraintsin the form of time constraints on portfolio selection and derives both optimal portfolio holdings and time allocation. Under time constraints, rational private investors make portfolio choices similar to those ofi nvestors with bounded rationality, i.e., insufficient diversification and excessive trading. Thus, time constraints offer an alternative, fully rational explanation for these real-world investment phenomena, which have to date been interpreted primarily in the light of behavioral finance. --excessive trading,insufficient diversification,learning,portfolio selection,time constraint

Generating a Target Payoff Distribution with the Cheapest Dynamic Portfolio: an Application to Hedge Fund Replication

This paper provides a new method to construct a dynamic optimal portfolio for asset management in a complete market. The method generates a target payoff distribution by the cheapest dynamic trading strategy. It is regarded as an extension of Dybvig (1988a) to continuous-time framework and dynamic portfolio optimization where the dynamic trading strategy is derived analytically by applying Malliavin calculus. As a practical example, the method is applied to hedge fund replication, which extends Kat and Palaro (2005) and Papageorgiou, Remillard and Hocquard (2008) to multiple trading assets with both long and short positions.

Is Online Trading Gambling with Peanuts?

Previous studies of investor behavior have documented that trading is harmful to the portfolio return, but have been unable to measure how important this underperformance is for the individual. By the use of detailed individual financial data, as well as trades from a Swedish online broker, I measure the cost of online trading. It is found that the more important the portfolio is, measured as the fraction of the investor's portfolio of total financial assets, the higher is turnover. In addition, financially important portfolios have slightly lower trading performance. The overall result suggest that the cost of online trading can be substantial. The top quintile of investors who have the highest share of their total financial assets in stocks invested at the brokerage firm under study loose 3.34% of financial wealth annually, which corresponds to 1.87% of aggregate income within this group. These investors do not only have lower overall wealth and income, but also have the highest aggregate trading losses. Therefore, trading losses are mainly carried by those who can afford them the least. Across individuals, annual losses for 36% of investors exceed 1% of their financial wealth, and 17% lose more than 5%.