Financial Prices Research Articles

Path shadowing Monte Carlo

We introduce a Path Shadowing Monte Carlo method, which provides the prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or ‘shadows’, the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called ‘Scattering Spectra’. This model promotes the diversity of generated paths while reproducing main statistical properties of financial prices, including stylized facts such as volatility roughness. Our method yields state-of-the-art predictions for future realized volatility and allows one to determine conditional option smiles for the S&P500 that outperform both the most recent low-parametric models and the option market itself. The code is available at https://github.com/RudyMorel/shadowing (This work is supported by the PRAIRIE 3IA Institute of the French ANR-19-P3IA-0001 program and the ENS-CFM models and data science chair.).

A Girsanov transformed Clark-Ocone-Haussmann type formula for $$L^1$$-pure jump additive processes and its application to portfolio optimization

AbstractWe derive a Clark-Ocone-Haussmann (COH) type formula under a change of measure for $$ L^1 $$ L 1 -canonical additive processes, providing a tool for representing financial derivatives under a risk-neutral probability measure. COH formulas are fundamental in stochastic analysis, providing explicit martingale representations of random variables in terms of their Malliavin derivatives. In mathematical finance, the COH formula under a change of measure is crucial for representing financial derivatives under a risk-neutral probability measure. To prove our main results, we use the Malliavin-Skorohod calculus in $$ L^0 $$ L 0 and $$ L^1 $$ L 1 for additive processes, as developed by Di Nunno and Vives (2017). An application of our results is solving the local risk minimization (LRM) problem in financial markets driven by pure jump additive processes. LRM, a prominent hedging approach in incomplete markets, seeks strategies that minimize the conditional variance of the hedging error. By applying our COH formula, we obtain explicit expressions for locally risk-minimizing hedging strategies in terms of Malliavin derivatives under the market model underlying the additive process. These formulas provide practical tools for managing risks in financial market price fluctuations with $$L^1$$ L 1 -additive processes.

Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.

Random Processes and Their Applications in Financial Mathematics

Random processes are fundamental tools in modern financial mathematics, widely applied in fields such as financial derivatives pricing, risk management, and interest rate models. This paper systematically introduces the basic theory of random processes, including definitions and classifications, Brownian motion and geometric Brownian motion, and stochastic differential equations. Furthermore, the paper explores specific applications of random processes in financial mathematics, focusing on financial derivatives pricing, risk management and portfolio optimization, and applications in interest rate models and credit risk. Through theoretical and empirical research, this paper reveals the significant role and development prospects of random processes in financial mathematics, providing new ideas and methods for risk management and pricing models in financial markets.

Financial ambiguity and oil prices

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices.

Optimizing composite early warning indicators

Research on predicting financial crises has produced various composite early warning indicators (EWIs) using macroeconomic and financial time-series. Much of the focus has been on identifying the best leading indicators for financial crises (e.g., credit-to-GDP ratios, financial asset prices, etc.). This paper instead focuses on how to optimally extract and combine signals from multiple cyclical indicators. We find that when combining multiple indicators into a composite EWI, jointly optimizing the indicators improves performance relative to optimizing individually and combining their signals. The performance of our jointly optimized EWIs is robust to the key modelling choices inherent in their design including the trend-cycle decomposition method and the preference for false positives over false negatives.

Mental time travel and the valuation of financial investments: analysing five biases that cause pricing anomalies

PurposeThe purpose of this paper is to analyse five biases in the valuation of financial investments using a mental time travel framework involving thought investments – with no objective time passing.Design/methodology/approachAn investment’s initial value, together with any periodic funding cash-flows, are mentally projected forward (at an expected rate of return) to give the value at the investment horizon; and this projected value is mentally discounted back to the present. If there is a difference between the initial and present values, then this can imply a bias in valuation.FindingsThe study identifies (and gives examples of) five real-world valuation biases: biased funding cash-flow estimates (e.g., mega infrastructure projects); biased rate of return projections (e.g., market crises, tech stock carve-outs); biased discount rate estimates (e.g., dual-listed shares, dual-class shares, short-termism, time-risk misperception, and long-termism); time-duration misestimation or perception bias when projecting (e.g., time-contracted projections which lead to short-termism); and time-duration misestimation or perception bias when discounting (e.g., time-extended discounting which also leads to short-termism). More than one bias can be operating at the same time and we give an example of low levels of retirement savings being the result of the biased discounting of biased projections. Finally, we consider the effects of the different biases of different agents operating simultaneously.Originality/valueThe paper examines key systematic misestimation and psychological biases underlying financial investment valuation pricing anomalies.

Drought and energy stock markets in the United States

Abstract Amid increasing concerns about the impact of climate change on financial asset pricing, this paper investigates how drought risk affects stock returns in the U.S. energy sector over a thirty-year period (1990–2019). To this end, we ranked energy companies based on the long-term trend of the Palmer drought severity index (PDSI) to compare stock performance between groups experiencing the most significant increases and decreases in drought risk. Our analysis indicates that firms located in drought-prone regions have lower stock returns than those in regions with decreasing drought risk, particularly since 2010. This finding is noteworthy, as the average PDSI trend gap between regions at high risk of drought and those at low risk has widened during this period. We further divide our sample of energy companies into two subsectors-fuel production and electric utility-and find that the stock prices of both subsectors have been negatively affected by drought risk since 2010. The study underscores the energy sector’s dependency on water availability, suggesting novel implications for policymakers and investors in assessing the financial impacts of drought risks in their climate-related decision-making.

FTSE 100 Index Linked Structured Financial Product Pricing Analysis

Since facing widespread skepticism after the 2008 economic crisis, nowadays structured products have gradually become the preferred investment choice for individual consumers, driven by the development of technology and economy. This study analyzes the price of the two most recent structured products offered by Lowes, which closely resemble the FTSE 100 index. It focuses on analyzing the differences in predicted returns resulting from modest structural changes and explores the best solutions for different time periods. The variability is determined by combining historical variability with the GARCH model, based on the principle of Risk Neutral Pricing Theory. The final result is acquired by the application of the Monte Carlo simulation method. Based on the trial results, product B has a greater yield rate compared to product A, although being more conditional. This compensates for the shortage and resulting in a much higher yield rate for product B. Furthermore, based on the findings on the projected output of various investment goods over different time periods, it can be observed that as the length increases, the anticipated return also increases. Ultimately, the sensitivity analysis reveals that the primary source of product risk stems from the impact of risk-free yields on this particular product.

Correlation Between CSR and Stock Market Performance: Analyzing 32 PSX Companies

This study examines the impact of Corporate Social Responsibility (CSR) on the financial performance and stock prices of 32 companies listed on the Pakistan Stock Exchange (PSX). Analysisysis focuses on the correlation between CSR activities and Return on Equity (ROE), investigating how different CSR strategies, such as donations, employee welfare, and cause-related marketing, influence stock market performance. The research employs both qualitative and quantitative methods, including interviews with senior executives and analysis of company financial statements. The findings reveal varying degrees of correlation between CSR initiatives and financial performance, with some companies showing significant positive correlations, while others exhibit negative or no correlation. The study also highlights challenges in obtaining CSR-related data and discusses the implications of CSR on the long-term financial stability of firms.

Capital flows-at-risk: Push, pull and the role of policy

We characterise the probability distributions of various types of gross capital flows conditional on information contained in financial asset prices in a panel of emerging market economies, with a focus on ‘tail’ events. Our framework, based on the quantile regression methodology, allows for a separate role of push- and pull-type factors. We find that both push and pull factors have heterogeneous effects across the distributions of gross capital flows, which are most marked in the left tails. We then explore the role of the pre-existing stance of macroprudential and capital flow management policy in shaping probability distributions of capital flows. Macroprudential and capital flow management measures can both be stabilising, leading to less volatile flows and in particular to lower chances of large portfolio outflows. A tighter macroprudential stance can also reduce the sensitivity of portfolio flows to global financial shocks.

Perancangan Strategi Perusahaan Berdasarkan Rasio Finansial pada PT Fajar Surya Wisesa Tbk Sebelum dan Sesudah Munculnya Covid-19

This study aims to design strategic recommendations based on financial ratios at PT Fajar Surya Wisesa Tbk before and after Covid-19. This research is motivated by a decline in global paper demand and production as well as a decline in the value of stock prices in the manufacturing sector. Particularly at PT Fajar Surya Wisesa Tbk. In this study, data was collected using the financial reports and stock prices of PT Fajar Surya Wisesa Tbk 12 quarters before the emergence of Covid-19 (April 2017–March 2020) and 12 quarters after the emergence of Covid-19 (April 2020 – March 2023). The data processing method used is the Wilcoxon Signed Ranks Test. The results of the Wilcoxon signed ranks test in the form of 9 financial ratio variables found a significant difference between the values before and after Covid-19. Meanwhile, for the 6 other financial ratio variables and stock prices, there is no significant difference between the values before and after Covid-19. After that, strategic recommendations are given based on test results, and a mapping is carried out on the Strengths and Weaknesses quadrants to get recommendations for strategies that are suitable for the company. In this study the authors provide recommendations on strategies that can be implemented by companies to improve financial performance.

Correlation Analysis Between Financial Ratios and Stock Prices of Real Estate Companies Listed on the Indonesia Stock Exchange

Correlation Analysis Between Financial Ratios and Stock Prices of Real Estate Companies Listed on the Indonesia Stock Exchange

Financial friction and optimal monetary policy: analysis of DSGE model with financial friction and price sticky

Financial friction and optimal monetary policy: analysis of DSGE model with financial friction and price sticky

Examining Dividend Policy as a Mediating Variable in Stock Price Trends: Evidence from the Indonesian Basic Materials

This research investigates the impact of financial ratios, including profitability, liquidity and solvency, on stock prices, with dividend policy as a mediator, in the Indonesian raw materials sector. The analysis covers 100 samples from 25 of companies listed on the Indonesia Stock Exchange (BEI) from 2019 to 2022. The research aims to explore stock price fluctuation trends and examine the relationship between financial ratios and stock prices, as well as the role of dividends policy in mediating this relationship. The findings show that profitability and liquidity are important factors in determining stock prices, while solvency does not influence stock prices, nor does dividend policy play an important role in mediating the relationship between variables. It is hoped that these findings will provide insight for investors, policy makers and industry stakeholders in navigating Indonesia's dynamic construction materials market.

Fuzzy Fractional Brownian Motion: Review and Extension

In traditional finance, option prices are typically calculated using crisp sets of variables. However, as reported in the literature novel, these parameters possess a degree of fuzziness or uncertainty. This allows participants to estimate option prices based on their risk preferences and beliefs, considering a range of possible values for the parameters. This paper presents a comprehensive review of existing work on fuzzy fractional Brownian motion and proposes an extension in the context of financial option pricing. In this paper, we define a unified framework combining fractional Brownian motion with fuzzy processes, creating a joint product measure space that captures both randomness and fuzziness. The approach allows for the consideration of individual risk preferences and beliefs about parameter uncertainties. By extending Merton’s jump-diffusion model to include fuzzy fractional Brownian motion, this paper addresses the modelling needs of hybrid systems with uncertain variables. The proposed model, which includes fuzzy Poisson processes and fuzzy volatility, demonstrates advantageous properties such as long-range dependence and self-similarity, providing a robust tool for modelling financial markets. By incorporating fuzzy numbers and the belief degree, this approach provides a more flexible framework for practitioners to make their investment decisions.

A STUDY ON COMPARING BORROWER EASE WITH STATE BANK OF INDIA (SBI) AND LIFE INSURANCE CORPORATION (LIC) HOUSING FINANCE IN KRISHNAGIRI DISTRICT

Everyone needs his own servant. The latter is an effective and valuable incentive for the financial turnaround of residency activities. But, buying a home in the current situation is a difficult task, as it has a significant use. A running Elegance family cannot afford the cost of this use. To take a decision on this movement, the government offers to extend housing finance to every individual at a low loan rate. The housing finance sector allows deduction in bonds. Home financing mode of housing on a credit basis in which we pay fixed payments often with high loan charges. A credit position was granted primarily based on the qualifications of the individuals and the standards of the bank. Many banks and financial institutions have entered the housing finance venues that offer modest advances at low borrowing costs. The cost of financing is an essential element of advances. Banks and economic institutions provide types of financial prices for credit ratings, for example, fixed and drifting. Banks provide housing finance as well as loans for development, growth, transformation, development etc. The main reason for this study is to think and dissect between housing finance technology and customer pleasure of SBI the largest public sector bank in India and LIC being the largest personal convenience financial institution in India.

Сбалансированность интересов участников распределительных отношений в контексте обеспечения региональной бюджетной безопасности

This study aims to clarify the concept of budget resilience and substantiate an approach for selecting indicators of regional budget resilience. A content analysis of scientific sources in the field of budget resilience was conducted, and current approaches to defining the concept of budget resilience were classified. The author’s interpretation of the concept is presented, emphasizing the need to select indicators that assess regional budget resilience by considering the interests of public funding recipients. Within this framework, groups of recipients—regional authorities, economic entities, and the population—are identified, and their interests are determined. A system of indicators is proposed to ensure budget resilience in the region, reflecting the level of interest satisfaction among these groups. A comparative analysis of the balance of recipients’ interests based on the results of budget policy and the budget process in the Russian Arctic regions was carried out, and trends in their economic interactions under conditions of global instability were analyzed. The study found vulnerabilities in the budget resilience system, manifested in the infringement of recipients’ interests. Changes in financial market conditions and resource prices led to a decrease in financial results for companies and a reduction in regional output redistributed through budgets, resulting in welfare loss for the regions and their populations. To address the situation, it is necessary to intensify investment activities and business development in the regions and optimize the fiscal regulation system, which will ultimately contribute to the growth of budget and household incomes. Changes in these indicators should be monitored not only in national currency but also in dollar terms to assess the recipients’ financial well-being on an international scale. Future research will focus on selecting specific indicators and their threshold values for assessing budget resilience in the Russian Arctic regions.

China's A-share Crash in 2015- -Based on the Perspective of the Liquidity Barrier Lake Caused by the "Targeted Easing" Policy

This paper is based on the theory of liquidity effect, that the 2015 crash is formed by directional easing liquidity of the lake, namely the central bank directional loose monetary policy derived monetary is not through monetary policy transmission channel to the entity neighborhood, but deposited in the financial system, pushing up the rising asset prices, and cause industrial "real to virtual", further boost the financial asset prices and the real economy fundamentals mismatch. Small and medium-sized enterprises and state-owned enterprises local government "hidden guarantee" of real estate, city investment companies in financing credit discrimination, and commercial Banks to lend behavior, also led to the liquidity of the formation of the lake finally, this paper summarizes a series of Suggestions, such as accelerating the marketization of interest rate reform, improve the monetary policy management, improve the macro-prudential monetary policy framework, establish and improve the multi-level capital market measures, to prevent such problems from happening again, make the central bank's monetary policy can more powerful through the financial system to the real economy, promote the development of our country economy with high quality.

Financial market concentration and misallocation

How does financial market concentration affect capital allocation? We propose a complete-markets model in which real investment and financial price impact are jointly determined in general equilibrium. We identify a two-way feedback mechanism whereby price impact induces misallocation and misallocation raises price impact. The mechanism is stronger if productivity is low or productivity dispersion is high. Given rising dispersion, the model can rationalize trends in corporate discount rates, cash holdings, investment, asset prices, and capital reallocation over the last two decades, even when market concentration is relatively stable. Overall, our findings suggest that financial market concentration may hamper allocative efficiency.