FLAIR: A Metric for Liquidity Provider Competitiveness in Automated Market Makers ArXiv ID: 2306.09421 “View on arXiv” Authors: Unknown Abstract This paper aims to enhance the understanding of liquidity provider (LP) returns in automated market makers (AMMs). LPs face market risk as well as adverse selection due to risky asset holdings in the pool that they provide liquidity to and the informational asymmetry between informed traders (arbitrageurs) and AMMs. Loss-versus-rebalancing (LVR) quantifies the adverse selection cost (Milionis et al., 2022a), and is a popular metric to evaluate the flow toxicity to an AMM. However, individual LP returns are critically affected by another factor orthogonal to the above: the competitiveness among LPs. This work introduces a novel metric for LP competitiveness, called FLAIR (short for fee liquidity-adjusted instantaneous returns), that aims to supplement LVR in assessments of LP performance to capture the dynamic behavior of LPs in a pool. Our metric reflects the characteristics of fee return-on-capital, and differentiates active liquidity provisioning strategies in AMMs. To illustrate how both flow toxicity, accounting for the sophistication of the counterparty of LPs, as well as LP competitiveness, accounting for the sophistication of the competition among LPs, affect individual LP returns, we propose a quadrant interpretation where all of these characteristics may be readily visualized. We examine LP competitiveness in an ex-post fashion, and show example cases in all of which our metric confirms the expected nuances and intuition of competitiveness among LPs. FLAIR has particular merit in empirical analyses, and is able to better inform practical assessments of AMM pools. ...

Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network ArXiv ID: 2306.08809 “View on arXiv” Authors: Unknown Abstract Optimal execution of a portfolio have been a challenging problem for institutional investors. Traders face the trade-off between average trading price and uncertainty, and traditional methods suffer from the curse of dimensionality. Here, we propose a four-step numerical framework for the optimal portfolio execution problem where multiple market regimes exist, with the underlying regime switching based on a Markov process. The market impact costs are modelled with a temporary part and a permanent part, where the former affects only the current trade while the latter persists. Our approach accepts impact cost functions in generic forms. First, we calculate the approximated orthogonal portfolios based on estimated impact cost functions; second, we employ dynamic program to learn the optimal selling schedule of each approximated orthogonal portfolio; third, weights of a neural network are pre-trained with the strategy suggested by previous step; last, we train the neural network to optimize on the original trading model. In our experiment of a 10-asset liquidation example with quadratic impact costs, the proposed combined method provides promising selling strategy for both CRRA (constant relative risk aversion) and mean-variance objectives. The running time is linear in the number of risky assets in the portfolio as well as in the number of trading periods. Possible improvements in running time are discussed for potential large-scale usages. ...

Deep Policy Gradient Methods in Commodity Markets ArXiv ID: 2308.01910 “View on arXiv” Authors: Unknown Abstract The energy transition has increased the reliance on intermittent energy sources, destabilizing energy markets and causing unprecedented volatility, culminating in the global energy crisis of 2021. In addition to harming producers and consumers, volatile energy markets may jeopardize vital decarbonization efforts. Traders play an important role in stabilizing markets by providing liquidity and reducing volatility. Several mathematical and statistical models have been proposed for forecasting future returns. However, developing such models is non-trivial due to financial markets’ low signal-to-noise ratios and nonstationary dynamics. This thesis investigates the effectiveness of deep reinforcement learning methods in commodities trading. It formalizes the commodities trading problem as a continuing discrete-time stochastic dynamical system. This system employs a novel time-discretization scheme that is reactive and adaptive to market volatility, providing better statistical properties for the sub-sampled financial time series. Two policy gradient algorithms, an actor-based and an actor-critic-based, are proposed for optimizing a transaction-cost- and risk-sensitive trading agent. The agent maps historical price observations to market positions through parametric function approximators utilizing deep neural network architectures, specifically CNNs and LSTMs. On average, the deep reinforcement learning models produce an 83 percent higher Sharpe ratio than the buy-and-hold baseline when backtested on front-month natural gas futures from 2017 to 2022. The backtests demonstrate that the risk tolerance of the deep reinforcement learning agents can be adjusted using a risk-sensitivity term. The actor-based policy gradient algorithm performs significantly better than the actor-critic-based algorithm, and the CNN-based models perform slightly better than those based on the LSTM. ...

Failure of Fourier pricing techniques to approximate the Greeks ArXiv ID: 2306.08421 “View on arXiv” Authors: Unknown Abstract The Greeks Delta and Gamma of plain vanilla options play a fundamental role in finance, e.g., in hedging or risk management. These Greeks are approximated in many models such as the widely used Variance Gamma model by Fourier techniques such as the Carr-Madan formula, the COS method or the Lewis formula. However, for some realistic market parameters, we show empirically that these three Fourier methods completely fail to approximate the Greeks. As an application we show that the Delta-Gamma VaR is severely underestimated in realistic market environments. As a solution, we propose to use finite differences instead to obtain the Greeks. ...

Using Internal Bar Strength as a Key Indicator for Trading Country ETFs ArXiv ID: 2306.12434 “View on arXiv” Authors: Unknown Abstract This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion to create a profitable trading strategy. Our findings suggest that IBS can be a useful technical indicator for predicting short-term price movements in this basket of ETFs. ...

Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships ArXiv ID: 2306.08157 “View on arXiv” Authors: Unknown Abstract Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. Six popular cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether are studied in this work. The proposed model’s performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, and support vector machines. The results show that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models. ...

Model-Free Market Risk Hedging Using Crowding Networks ArXiv ID: 2306.08105 “View on arXiv” Authors: Unknown Abstract Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio. ...

Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization ArXiv ID: 2306.07013 “View on arXiv” Authors: Unknown Abstract Reinforcement learning (RL) based investment strategies have been widely adopted in portfolio management (PM) in recent years. Nevertheless, most RL-based approaches may often emphasize on pursuing returns while ignoring the risks of the underlying trading strategies that may potentially lead to great losses especially under high market volatility. Therefore, a risk-manageable PM investment framework integrating both RL and barrier functions (BF) is proposed to carefully balance the needs for high returns and acceptable risk exposure in PM applications. Up to our understanding, this work represents the first attempt to combine BF and RL for financial applications. While the involved RL approach may aggressively search for more profitable trading strategies, the BF-based risk controller will continuously monitor the market states to dynamically adjust the investment portfolio as a controllable measure for avoiding potential losses particularly in downtrend markets. Additionally, two adaptive mechanisms are provided to dynamically adjust the impact of risk controllers such that the proposed framework can be flexibly adapted to uptrend and downtrend markets. The empirical results of our proposed framework clearly reveal such advantages against most well-known RL-based approaches on real-world data sets. More importantly, our proposed framework shed lights on many possible directions for future investigation. ...

From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance ArXiv ID: 2307.01155 “View on arXiv” Authors: Unknown Abstract In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech. In that sense, we first introduce the general overview of blockchain with its main cryptographic primitives such as digital signature algorithms, hash functions, and random number generators as well as the security vulnerabilities of blockchain technologies after the merge of quantum computers considering Shor’s quantum factoring and Grover’s quantum search algorithms. We then discuss the privacy preserving quantum-resistant blockchain systems via threshold signatures, ring signatures, and zero-knowledge proof systems i.e. ZK-SNARKs in quantum resistant blockchains. After emphasizing the difference between the quantum-resistant blockchain and quantum-safe blockchain we mention the security countermeasures to take against the possible quantumized attacks aiming these systems. We finalize our discussion with quantum blockchain, efficient quantum mining and necessary infrastructures for constructing such systems based on quantum computing. This review has the intention to be a bridge to fill the gap between quantum computing and one of its most prominent application realms: Finance. We provide the state-of-the-art results in the intersection of finance and quantum technology for both industrial practitioners and academicians. ...

Making forecasting self-learning and adaptive – Pilot forecasting rack ArXiv ID: 2306.07305 “View on arXiv” Authors: Unknown Abstract Retail sales and price projections are typically based on time series forecasting. For some product categories, the accuracy of demand forecasts achieved is low, negatively impacting inventory, transport, and replenishment planning. This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories. We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear. The Knitwear product category has a current demand forecast accuracy from non-AI models in the range of 60%. We explored how to improve the forecast accuracy using a rack approach. To generate forecasts, our decision model dynamically selects the best algorithm from an algorithm rack based on performance for a given state and context. Outcomes from our AI/ML forecasting model built using advanced feature engineering show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%. Because our rack comprises algorithms that cater to a range of customer data sets, the forecasting model can be easily tailored for specific customer contexts. ...