Ren-Raw Chen

Fund similarity is important for investors when constructing diversified portfolios. Because mutual funds do not always adhere closely to their stated investment policies, investors may unintentionally hold funds with overlapping exposures, leading to reduced diversification and instead causing “diworsification”, which is an investment term for when too much complexity leads to worse results. As a result, various quantitative methods have been proposed in the literature to investigate fund similarity, primarily using portfolio holdings. Recently, machine learning tools such as clustering and graph theory have been introduced to capture fund similarity. This paper builds on this literature by applying bipartite graphs and node2vec embeddings to a comprehensive dataset that covers 3874 funds over a nearly 6-year period. Our empirical evidence suggests that, bipartiteness is not preserved for non-index (active) funds. Furthermore, while graph embeddings yield higher similarity scores than holding-based measures, they do not necessarily outperform holding-based similarity in explaining returns. These findings suggest that graph-based embeddings capture structural relationships among funds that are distinct from direct portfolio overlap but are not sufficient substitutes when similarity is evaluated solely through returns. As a result, we recommend a more comprehensive similarity measure that includes important risk metrics such as volatility risk, liquidity risk, and systemic risk.

Utilizing a dataset of 190 risk factors spanning over three decades, we apply a swarm-based classification model to estimate factor velocity and analyze its implications for asset pricing. Our results show that slower-moving factors generate higher abnormal returns than their faster-moving counterparts, underscoring the critical role of price adjustment speed in market dynamics. Furthermore, our results suggest that trading frictions impede the rapid assimilation of information, contributing to the observed return patterns. This research offers new insights into return predictability and demonstrates the potential of swarm intelligence as a powerful tool for financial modeling.

Using artificial intelligence tools to evaluate financial derivatives has become increasingly popular. PSO (particle swarm optimization) is one such tool. We present a comprehensive study of PSO for pricing American options on GPUs using OpenCL. PSO is an increasingly popular heuristic for financial parameter search; however, its high computational cost (especially for path-dependent derivatives) poses a challenge. We review PSO-based pricing and survey prior GPU acceleration efforts. We then describe our OpenCL optimization pipeline on an Apple M3 Max GPU (OpenCL 1.2 via PyOpenCL 2024.1). Starting from a NumPy baseline (36.7 s), we apply successive enhancements: an initial GPU offload (8.0 s), restructuring loops (forward/backward) to minimize divergence (2.3 s → 0.95 s), kernel fusion (0.94 s), and explicit SIMD vectorization (float4) (0.25 s). The fully fused float4 kernel achieves 0.246 s, a ~150X speedup over CPU. We analyzed all eight intermediate kernels (named by file), detailing techniques (memory coalescing, branch avoidance, etc.) and their effects on throughput. Our results exceed prior art in speed and vector efficiency, illustrating the power of combined OpenCL strategies.

The market of swaptions is the largest option market in the financial world. The market is liquid and serves as a benchmark for other exotic interest rate derivatives. As a result, a fast valuation algorithm is essential to the valuation of swaptions. In a recent article, Chen, Hsieh, and Huang (2017) derive a closed-form solution to the at-the-month swaptions by assuming a deterministic log-normal volatility process.

Predicting future price movements has always been one of the major topics in financial research, and there is no better method to predict the future prices of an asset than using its derivatives. In this paper, we propose a model-free lattice model that describes the complete price evolution of the underlying asset and simultaneously re-prices all of its European options. Given that such a lattice is consistent with market option prices, it must embed all necessary risk factors (e.g., random volatility, random interest rates, and jumps) and market restrictions (e.g., mean-reversion and liquidity) that are priced into the European options.

It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence—Boids and PSO (particle swarm optimization)—to study the herding behavior. Our results indicate weak swarm among brokers/dealers.

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk.

Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining firm search through simulation models typically build a performance landscape. The NK model is the leading tool used for this purpose in the management science literature. We assess the usefulness of the NK landscape for simulated swarm search. We find that the strength of the swarm model for examining firm search and decision-choice problems—the ability to model the influence of other firms on the focal firm—is limited to the NK landscape. Researchers will need alternative ways to create a performance landscape in order to use our full swarm model in simulations. We also identify multiple opportunities—endogenous landscapes, agent-specific landscapes, incomplete information, and costly movements—that future researchers can include in landscape development to gain the maximum insights from swarm-based firm search simulations.

In this article, the authors apply graphics processing unit (GPU) computation to an American option pricing problem via Monte Carlo (MC) simulations and particle swarm optimization (PSO). Given that computations in both MC and PSO can be vectorized and made independent, the valuation can be readily performed on GPUs. As a result, we can increase the accuracy of the valuation by increasing MC paths and particles without spending more time. For example, with a large number of particles (but allocated to GPUs), convergence can be reached in very few steps. The method introduced in this article can be extended to a wide variety of exotic derivatives or a large portfolio of diverse derivatives (known as an eigen portfolio). This is helpful in both trading and risk management.

Based on the Chen, Hsieh, and Huang (2017) interest rate model, this research explores the analytical approach for pricing CMS spread options. We first derive a complex joint density for two swap rates composed of sequential forward rates and approximate the joint density by bivariate normals. After applying the methods of Pearson (1995) and Li, Deng, and Zhou (2008), we obtain two analytical pricing models and examine their accuracy using numerical analysis. Finally, we empirically show the predictive power of the implied volatility of CMS options for future economic states.