The air in Kraków is one of the most polluted in Europe. Polish standards for notification and alert levels for PM10 particulate matter are one of the the highest in Europe and exceed the recommendations of the World Health Organization (WHO) for safe daily con - centrations by several times. The article presents the results of airborne dust measurements in three districts of Kraków. The study has shown that the concentration of PM2.5 and PM10 particulate matter exceeded the annual average permissible levels. Empirical mea - surements of PM2.5 show significantly higher air pollution values than the data notified by stationary monitoring stations installed in two locations. The high value of Pearson linear correlation coefficient confirms that weather conditions have a significant impact on air quality in Kraków. Wind speed in the autumn and winter seasons has by far the greatest influence on air quality in al. Krasińskiego, in the Ruczaj and Kurdwanów districts. A strong negative correlation was displayed. Manual measurements should be used to verify data obtained from air monitoring stations. It is to be expected that, in Kraków, air purity will improve due to the implementation of an anti-smog resolution and subsidies for the replacement of obsolete heating systems with more environmentally friendly solutions.
This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies.
This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India.
In this research paper, we investigate into a paper named "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [arXiv:1706.10059]. It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial-model-free reinforcement learning framework, which consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. Three different instants are used to realize this framework, namely a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). The performance is then examined by comparing to a number of recently reviewed or published portfolio-selection strategies. We have successfully replicated their implementations and evaluations. Besides, we further apply this framework in the stock market, instead of the cryptocurrency market that the original paper uses. The experiment in the cryptocurrency market is consistent with the original paper, which achieve superior returns. But it doesn't perform as well when applied in the stock market.
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to changes in some key parameters.
We study a robust utility maximization problem in a general discrete-time frictionless market under quasi-sure no-arbitrage. The investor is assumed to have a random and concave utility function defined on the whole real-line. She also faces model ambiguity on her beliefs about the market, which is modeled through a set of priors. We prove the existence of an optimal investment strategy using only primal methods. For that we assume classical assumptions on the market and on the random utility function as asymptotic elasticity constraints. Most of our other assumptions are stated on a prior-by-prior basis and correspond to generally accepted assumptions in the literature on markets without ambiguity. We also propose a general setting including utility functions with benchmark for which our assumptions are easily checked.
We describe the polarization topology of the vector beams emerging from a patterned birefringent liquid crystal plate with a topological charge q at its center (q-plate). The polarization topological structures for different q-plates and different input polarization states have been studied experimentally by measuring the Stokes parameters point-by-point in the beam transverse plane. Furthermore, we used a tuned q=1/2-plate to generate cylindrical vector beams with radial or azimuthal polarizations, with the possibility of switching dynamically between these two cases by simply changing the linear polarization of the input beam.
In this paper, we consider an informational market model with two flows of informations. The smallest flow F, which is available to all agents, is the filtration of the initial market model(S,F,P), where S is the assets' prices and P is a probability measure. The largest flow G contains additional information about the occurrence of a random time T. This setting covers credit risk theory where T models the default time of a firm, and life insurance where T represents the death time of an insured. For the model (S-S^T,G,P), we address the log-optimal portfolio problem in many aspects. In particular, we answer the following questions and beyond: 1) What are the necessary and sufficient conditions for the existence of log-optimal portfolio of the model under consideration? 2) what are the various type of risks induced by T that affect this portfolio and how? 3) What are the factors that completely describe the sensitivity of the log-portfolio to the parameters of T? The answers to these questions and other related discussions definitely complement the work of Choulli and Yansori [12] which deals with the stopped model (S^T,G).
A general model is presented for coupling of high-Q whispering-gallery modes in optical microsphere resonators with coupler devices that possess a discrete and continuous spectrum of propagating modes. By contrast to conventional high-Q optical cavities, in microspheres the independence of high intrinsic quality-factor and controllable parameters of coupling via an evanescent field offer a variety of regimes similar to those that are already available in rf devices. The theory is applied to data reported earlier on different types of couplers to microsphere resonators and is complemented by the experimental demonstration of enhanced coupling efficiency (∼80%) and variable loading regimes with Q>108 fused-silica microspheres.