Relative measures of team heterogeneity commonly used in economics and finance tend to capture dominance or concentration—the extent to which one member is superior to all others. We show that, by modifying the denominator, alternative measures can rather capture disparity or spread—the extent to which half the members are superior to the other half. We illustrate empirically the benefits of distinguishing between dominance-heterogeneity and disparity-heterogeneity in characterizing the composition of venture capital syndicates.
How to manage the non-dividend behavior of state-owned enterprises is not only a key part of protecting the interests of small and medium-sized investors, but also an inevitable requirement for implementing the mandatory dividend policy and deepening the reform of state-owned enterprises in transitional economies. This paper takes China as representative of transitional economies, discusses the impact of mixed ownership reform on the dividend behavior of state-owned enterprises from the two dimensions of shareholding ratio and appointed executives, and manually collects data on the nature, shareholding ratio and appointed executives of the top ten shareholders disclosed in the annual reports. The empirical findings are as follows: (1) Appointing executives to state-owned enterprises by non-state-owned shareholders can significantly enhance the cash dividend level of state-owned enterprises, while simply increasing the shareholding ratio of non-state-owned shareholders has no significant effect on the dividend level of state-owned enterprises. (2) After local governments launch debt governance actions, the role of mixed ownership reform in improving the dividend level of state-owned enterprises has weakened. (3) In regions with high marketization level and media attention, local government debt governance has a particularly significant negative moderating effect on the positive correlation between mixed ownership reform and the dividend level of state-owned enterprises. This paper will not only better promote the implementation of various policies of the reform of state-owned enterprises to obtain the best implementation efficiency, but also provide more beneficial enlightenment for the protection of the interests of small and medium-sized investors in transitional economies.
A classic problem in finance is the question of how an investor should optimally allocate wealth over one risky and one risk-free asset. This article reconsiders this theory under the assumptions that (1) the investor’s objective is to maximize the intertemporal growth rate of their wealth, (2) that the investor can make portfolio adjustments only in discrete time, and (3) that the risky asset is defined in each period according to only its mean return and the standard deviation thereof. This problem has been resolved only for continuous time. Here, the discrete time strategy for optimal capital growth is resolved for two different methodologies, and it is shown that the continuous time model is recoverable as a limit version of each of the two discrete time models. Finally, both of the methodologies considered lead to the same first-order Taylor’s approximation formula, which always gives a more accurate approximation to the true optimal discrete time strategy than does the continuous time strategy.
We introduce Onflow, a reinforcement learning method for optimizing portfolio allocation via gradient flows. Our approach dynamically adjusts portfolio allocations to maximize expected log returns while accounting for transaction costs. Using a softmax parameterization, Onflow updates allocations through an ordinary differential equation derived from gradient flow methods. This algorithm belongs to the large class of stochastic optimization procedures; we measure its efficiency by comparing our results to the mathematical theoretical values in a log-normal framework and to standard benchmarks from the ’old NYSE’ dataset. For log-normal assets with zero transaction costs, Onflow replicates Markowitz optimal portfolio, achieving the best possible allocation. Numerical experiments from the ’old NYSE’ dataset show that Onflow leads to dynamic asset allocation strategies whose performances are: a) comparable to benchmark strategies such as Cover’s Universal Portfolio or Helmbold et al. “multiplicative updates” approach when transaction costs are zero, and b) better than previous procedures when transaction costs are high. Onflow can even remain efficient in regimes where other dynamical allocation techniques do not work anymore. Onflow is a promising portfolio management strategy that relies solely on observed prices, requiring no assumptions about asset return distributions. This makes it robust against model risk, offering a practical solution for real-world trading strategies.
Stock market traders who trade because of information they possess reveal that information to the rest of the market in the process of bidding: if the information is positive they bid up the price, and if it is negative they lower it. New information constantly develops and is brought to the market in this way, and because it influences prices, it ultimately influences the allocation of investments by firms. Using a new approach, we estimate the flow of this information and the price of that information (different from the stock price), and thus the total value of that information, for each stock, and then sum up this value across all stocks, obtaining an estimate of the total value of the dynamic flow of information in the stock market as a whole. This requires digesting the records of millions of stock orders (including cancelled orders, not just executed trades) to construct the dynamic limit order book and estimate the information flow and value from its structure. Our results support the notion that the cross-correlation of price impact across stocks is consistent with the capital asset pricing model: there is a single systematic component of price impact, and this is driven by the volatility of the systematic component of the stock market. This result suggests that by separating the underlying information into two components, systematic and idiosyncratic, informed traders distinguish between productive assets that have a systematic impact on the economy and those that can be diversified.
