A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Libra ensures fair order-matching in electronic financial exchanges.
In this paper, we propose a minimal model beyond geometric Brownian motion that aims to describe price actions with market inefficiency. From simple financial theory considerations, we arrive at a simple two-variable hidden Markovian time series model, with one of the variable entirely unobserved. Then, we analyze the …
Many studies have shown that there are good reasons to claim very low predictability of currency nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J.James, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the t…
Study finds cryptoasset markets inefficient due to capital reallocation frictions.
Study minimizes market inefficiency in systemic economies.
Algorithm beats sports betting markets, showing inefficiencies.
Deep learning model predicts stock price movements based on historical data.
We study the effects of introducing information inefficiency in a model for a random linear economy with a representative consumer. This is done by considering statistical, instead of classical, economic general equilibria. Employing two different approaches we show that inefficiency increases the consumption set of a …
Study reveals inefficiencies in EU carbon trading market.
The paper explores fair treatment in financial exchanges, finding unbounded fairness unrealistic and proposing ε-fairness as a solution.
Social learning can make financial markets inefficient, but individual learning can fix this.
Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.
This paper examines unfair trading practices in NFT markets.
Paper develops efficient DML estimators for multiway clustered data without cross-fitting.
Investors trade based on shifting prices, leading to market inefficiencies.
I summarize the recent work on market (in)efficiency, highlighting key elements why financial markets will never be made efficient. My approach is not by adding more empirical evidence, but giving plausible reasons as to where inefficiency arises and why it's not rational to arbitrage it away.
The efficient market hypothesis has been considered one of the most controversial arguments in finance, with the academia divided between who claims the impossibility of beating the market and who believes that it is possible to gain over the average profits. If the hypothesis holds, it means, as suggested by Burton Ma…
We present some indications of inefficiency of the Brazilian stock market based on the existence of strong long-time cross-correlations with foreign markets and indices. Our results show a strong dependence on foreign markets indices as the S\&P 500 and CAC 40, but not to the Shanghai SSE 180, indicating an intricate i…
Market inefficiencies persist in DEXes, especially during high volatility.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
In this paper we examine inefficiencies and information disparity in the Japanese stock market. By carefully analysing information publicly available on the internet, an `outsider' to conventional statistical arbitrage strategies--which are based on market microstructure, company releases, or analyst reports--can never…
We discuss the stationary states of a model economy in which heterogeneous adaptive consumers purchase commodity bundles repeatedly from sellers. The system undergoes a transition from an inefficient to an efficient state as the number of consumers increases. In the latter phase, however, price fluctuations may…
Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The inefficiency is caused by the backpropagation algorithm's forward locking, backward locking, and update locking problems. Existing solutions for a…
Detecting adversarial examples is as hard as classifying them.
Study measures irreversibility in crypto trends using Kullback-Leibler divergence.
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
Study analyzes EU ETS carbon market dynamics, revealing inefficiencies and anomalies.
This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.
Abstract: A new approach to technical indicators without lag.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
Revisits life insurance surplus models with new technical bases.
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
We introduce a stochastic price model where, together with a random component, a moving average of logarithmic prices contributes to the price formation. Our model is tested against financial datasets, showing an extremely good agreement with them. It suggests how to construct trading strategies which imply a capital g…
Weak form of the Efficiency Market Hypothesis (EMH) excludes predictions of future market movements from historical data and makes the technical analysis (TA) out of law. However the technical analysis is widely used by traders and speculators who steadely refuse to consider the market as a "fair game" and survive with…
Myopic investors make suboptimal choices that benefit others, leading to market inefficiencies.
This work presents an asset pricing model that under rational expectation equilibrium perspective shows how, depending on risk aversion and noise volatility, a risky-asset has one equilibrium price that differs in term of efficiency: an informational efficient one (similar to Campbell and Kyle (1993)), and another one …
Predicting stock jumps using liquidity and technical indicators.
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
Study confirms mispricing in sportsbooks but finds data issues affect results.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Naive investors make riskier choices than optimal strategies in continuous-time finance.
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
The optimal (`equilibrium') macroscopic properties of an economy with industries endowed with different technologies, commodities and one consumer are derived in the limit with fixed using the replica method. When technologies are strictly inefficient, a phase transition occurs upon increas…
New method improves efficiency analysis with big data.
This paper tackles hidden technical debts in fair ML systems for Fintech.
CFM fee income is insufficient to hedge market risk, study finds.