Xiongfei Jiang | Searcharxiv

arXiv2026

Effects of analyst sentiment on volatility dynamics in financial market

Text emotions are extracted using natural language processing technique on a substantial corpus of analyst reports on the Chinese stock market. Subsequently, the text-based analyst sentiment indices are constructed. It is observed that both optimistic and pessimistic sentiments represent short-range memory. Optimistic and pessimistic sentiments are correlated with volatility positively and negatively, respectively. The analysis of transfer entropy reveals that past pessimistic sentiment affects future volatility. Further, we model the driving effect of analyst sentiment on volatility using a GARCH model. The results show that pessimistic sentiment is an explanatory factor for volatility, while optimistic sentiment is not.

physics.soc-ph↗

arXiv2025

L-Sort: On-chip Spike Sorting with Efficient Median-of-Median Detection and Localization-based Clustering

Spike sorting is a critical process for decoding large-scale neural activity from extracellular recordings. The advancement of neural probes facilitates the recording of a high number of neurons with an increase in channel counts, arising a higher data volume and challenging the current on-chip spike sorters. This paper introduces L-Sort, a novel on-chip spike sorting solution featuring median-of-median spike detection and localization-based clustering. By combining the median-of-median approximation and the proposed incremental median calculation scheme, our detection module achieves a reduction in memory consumption. Moreover, the localization-based clustering utilizes geometric features instead of morphological features, thus eliminating the memory-consuming buffer for containing the spike waveform during feature extraction. Evaluation using Neuropixels datasets demonstrates that L-Sort achieves competitive sorting accuracy with reduced hardware resource consumption. Implementations on FPGA and ASIC (180 nm technology) demonstrate significant improvements in area and power efficiency compared to state-of-the-art designs while maintaining comparable accuracy. If normalized to 22 nm technology, our design can achieve roughly $\times 10$ area and power efficiency with similar accuracy, compared with the state-of-the-art design evaluated with the same dataset. Therefore, L-Sort is a promising solution for real-time, high-channel-count neural processing in implantable devices.

eess.SP↗

arXiv2017

Transfer entropy between communities in complex networks

With the help of transfer entropy, we analyze information flows between communities of complex networks. We show that the transfer entropy provides a coherent description of interactions between communities, including non-linear interactions. To put some flesh on the bare bones, we analyze transfer entropies between communities of five largest financial markets, represented as networks of interacting stocks. Additionally, we discuss information transfer of rare events, which is analyzed by Rényi transfer entropy.

q-fin.ST↗