arXiv research

Study reveals inefficiencies in EU carbon trading market.

Study finds cryptoasset markets inefficient due to capital reallocation frictions.

Study minimizes market inefficiency in systemic economies.

Algorithm beats sports betting markets, showing inefficiencies.

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…

New method improves efficiency analysis with big 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 …

Social learning can make financial markets inefficient, but individual learning can fix this.

Study confirms mispricing in sportsbooks but finds data issues affect results.

Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.

Investors trade based on shifting prices, leading to market inefficiencies.

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 …

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…

New asymptotic e-values improve inference by eliminating data-dependent scaling inefficiency.

New method improves combinatorial optimization by overcoming inefficient sampling.

Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient whe…

Paper proposes a novel method to improve matrix completion with median loss for large datasets.

The Moscow Stock Exchange was inefficient for most of 2012-2021.

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.

We discuss the stationary states of a model economy in which $N$ heterogeneous adaptive consumers purchase commodity bundles repeatedly from $P$ 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…

Detecting adversarial examples is as hard as classifying them.

In recent years, data has played an increasingly important role in the economy as a good in its own right. In many settings, data aggregators cannot directly verify the quality of the data they purchase, nor the effort exerted by data sources when creating the data. Recent work has explored mechanisms to ensure that th…

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.

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity mark…

This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.

Paper proposes DSP to accelerate deep learning model training by addressing locking and straggler problems.

Study shows popular RL methods are inefficient due to unfair baselines.

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…

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 …

End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning system is specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all "peripheral" modules like repre…

Benanza speeds up DL model optimization by automatically generating micro-benchmarks and identifying inefficiencies.

Naive investors make riskier choices than optimal strategies in continuous-time finance.

The optimal (`equilibrium') macroscopic properties of an economy with $N$ industries endowed with different technologies, $P$ commodities and one consumer are derived in the limit $N\to\infty$ with $n=N/P$ fixed using the replica method. When technologies are strictly inefficient, a phase transition occurs upon increas…

CFM fee income is insufficient to hedge market risk, study finds.

Meta-RL learns shared and task-specific information for quick adaptation.

Study confirms Indian stock market is weak form inefficient.

Increase Alpha uses deep learning to predict stock movements efficiently.

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models th…

We study the behavior of simple models for financial markets with widely spread frequency either in the trading activity of agents or in the occurrence of basic events. The generic picture of a phase transition between information efficient and inefficient markets still persists even when agents trade on widely spread …

Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space. Existing algorithms l…

Bayesian GNNs with temperature improve prediction efficiency in CP.

The focal point of this paper is the issue of "drawdown" which arises in recursive betting scenarios and related applications in the stock market. Roughly speaking, drawdown is understood to mean drops in wealth over time from peaks to subsequent lows. Motivated by the fact that this issue is of paramount concern to co…

Study finds Bitcoin market efficient, no exploitable inefficiencies with neural networks.