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Xiong-Fei Jiang

Publications and source records attributed to Xiong-Fei Jiang.

5 recordsLinked to original sources

Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents

The short-board effect, analogous to Liebig's Law of the Minimum, postulates that the collective performance of a team is constrained by its weakest component. This principle has profound implications for the optimization of collaboration in a variety of contexts, including management, education, and organizational structures. Despite its theoretical significance, empirical validation remains elusive due to challenges of assessing individual capabilities, controlling real-world variables, and data biases towards successful outcomes, as well as high employee turnover.To address this absence of knowledge, we employ multi-agents driven by large language models to simulate a teamwork with standard operating procedure, revealing the relationship between individual capability and collective team performance.In homogeneous team configurations, three capability regimes are observed, particularly the Sisyphus predicament state at the critical capability threshold characterized by extensive ineffective efforts and pseudo-high efficiency. Furthermore, with a single weak link quantifying the short-board effect, we highlight different impacts across core and non-core members on the team performance.More importantly, when the team exhibits multiple weak links, a cumulative product effect emerges, demonstrating that team performance is shaped by the aggregated impact of all weaknesses rather than the weakest link solely.This suggests that mitigation strategies should extend beyond the remediation of individual weak links.These findings rigorously elaborate the short-board theory and provide actionable insights to optimize team management, organizational operations, and supply chain resilience.

physics.soc-ph↗

Dynamic structure of stock communities: A comparative study between stock returns and turnover rates

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates.

q-fin.ST↗

Dynamic portfolio strategy using clustering approach

The problem of portfolio optimization is one of the most important issues in asset management. This paper proposes a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: selecting the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, i.e., degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion, then using the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index or the sum of the amplitudes of the trading days with rising index to the total number of trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that the peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all the possible optimal portfolio strategy based on different parameters to select portfolios and different criteria to identify market conditions, $65\%$ of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market and the proportion is $70\%$ for the Shenzhen A-Share market.

q-fin.ST↗

Geography and distance effect on financial dynamics in the Chinese stock market

Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical parameter, the stock location is found to have an impact on the financial dynamics, except for the financial crisis time of the Shenzhen market. Stock distance effect is further studied, with a crossover behavior observed for the stock distance distribution. The probability of the short distance is much greater than that of the long distance. The average stock correlation is found to weakly decay with the stock distance for the Shanghai stock market, but stays nearly stable for different stock distance for the Shenzhen stock market.

physics.soc-ph↗

How volatilities nonlocal in time affect the price dynamics in complex financial systems

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.

q-fin.GN↗