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This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to…
It is a difficult task for both professional investors and individual traders continuously making profit in stock market. With the development of computer science and deep reinforcement learning, Buy\&Hold (B\&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose…
We study opportunistic optimal liquidation over fixed deadlines on BTC-USD limit-order books (LOB). We present RL-Exec, a PPO agent trained on historical replays augmented with endogenous transient impact (resilience), partial fills, maker/taker fees, and latency. The policy observes depth-20 LOB features plus microstructure indicators and acts under a sell-only inventory constraint to reach a residual target. Evalua…
In decentralized finance (DeFi), designing fixed-income lending automated market makers (AMMs) is extremely challenging due to time-related complexities. Moreover, existing protocols only support single-maturity lending. Building upon the BondMM protocol, this paper argues that its mathematical invariants are sufficiently elegant to be generalized to arbitrary maturities. This paper thus propose an improved design, B…
This study provides a practical introduction to high-frequency trading in blockchain-based currency markets. These types of markets have some specific characteristics that differentiate them from the stock markets, such as a large number of trading exchanges (centralized and decentralized), relative simplicity in moving funds from one exchange to another, and the large number of new currencies that have very little l…
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relat…
This paper proposes a two-stage decision support system for long-short portfolio optimization under environmental, social, and governance (ESG) considerations. In the first stage, assets are evaluated using a multi-criteria procedure based on TODIMSort, with criterion weights derived using the MEREC (Removal Effects of Criteria) method. This allows assets to be assigned to classes ordered according to preferences tha…
Ship finance is a data-intensive and document-heavy segment of asset-based lending, requiring the integration of financial, technical, contractual, and regulatory information from heterogeneous and largely unstructured sources. Increasing environmental regulation and ESG reporting requirements are adding further complexity to underwriting and loan-origination processes. Recent advances in artificial intelligence (AI)…
Financial advisors and investors struggle with information overload from financial news, where irrelevant content and noise obscure key market signals and hinder timely investment decisions. To address this, we propose a novel Chain-of-Thought (CoT) summarization framework that condenses financial news into concise, event-driven summaries. The framework integrates user-specified keywords to generate personalized outp…
This study introduces a portfolio optimization framework to minimize mixed conditional value at risk (MCVaR), incorporating a chance constraint on expected returns and limiting the number of assets via cardinality constraints. A robust MCVaR model is presented, which presumes ellipsoidal support for random returns without assuming any distribution. The model utilizes an uncertainty set grounded in a reproducing kerne…
Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and d…
As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT), staking and mining protocols have emerged. A financial ecosystem built on top of a blockchain is supposed to be fair and transparent for each participating actor. Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their…
Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a tak…
Biofouling of ships causes major environmental and economic consequences all over the world. In addition, biofouling management of ship hulls causes both social, environmental and economic risks that should all be considered reaching well-balanced decisions. In addition, each case is unique and thus optimal management strategy must be considered case-specifically. We produced a novel decision support tool using Bayes…
Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid anal…
Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various c…
Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. Fluctuations of high frequency exchange rates of eight major world currencies over 2010-2018 period are used to study cross-correlations. The study is motivated by fundamental questions in complex systems' response to significant environmental changes and by potential applications in investmen…
Modern investigation in economics and in other sciences requires the ability to store, share, and replicate results and methods of experiments that are often multidisciplinary and yield a massive amount of data. Given the increasing complexity and growing interaction across diverse bodies of knowledge it is becoming imperative to define a platform to properly support collaborative research and track origin, accuracy …
An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.
Hallucination is fluent generation that is not supported by the source or the world — a likelihood-trained model completing a pattern, not a database lookup.
PPO is a policy-gradient algorithm that clips the probability ratio so each update stays close to the previous policy, giving much of TRPO’s stability with first-order SGD.
A CTA that tries to pick turns — anticipatory shorts of highs and buys of lows — the opposite personality of a breakout shop.
Enter long on an N-day high and short on an N-day low; exit on a shorter M-day opposite extreme — Richard Donchian’s channel, still the skeleton of many CTA breakouts.
General collateral (GC) is repo against a basket of acceptable Treasuries (or other eligible bonds) rather than a specific CUSIP — the opposite of specials.
Comparative advantage says a country (or desk) should specialize in the activity with the lowest opportunity cost, even if it is worse at everything in absolute terms.
Opportunity cost is the value of the next-best alternative you give up when you choose one use of a scarce resource — time, capital, balance-sheet, or a risk limit.
Household Savings Rate — Aggregate saving that supports or constrains future consumption and risk asset demand.
Private Equity Dry Powder — Undeployed PE capital that can support LBO activity and credit demand.
Rising Stars — High-yield upgrades into investment grade, often supporting spread tightening episodes.
A Financial Conditions Index aggregates variables such as rates, credit spreads, equities, and the dollar to measure how supportive or restrictive the market environment is for growth and risk assets.
Implementation shortfall is the gap between a decision price (or arrival price) and the actual average execution price, including missed-trade opportunity cost.
Idiosyncratic risk is residual variance after the factors — name-specific noise that diversification is supposed to shrink.
Comparative advantage says a country (or desk) should specialize in the activity with the lowest opportunity cost, even if it is worse at everything in absolute terms.
A CTA that tries to pick turns — anticipatory shorts of highs and buys of lows — the opposite personality of a breakout shop.
Enter long on an N-day high and short on an N-day low; exit on a shorter M-day opposite extreme — Richard Donchian’s channel, still the skeleton of many CTA breakouts.
An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.
A Financial Conditions Index aggregates variables such as rates, credit spreads, equities, and the dollar to measure how supportive or restrictive the market environment is for growth and risk assets.
General collateral (GC) is repo against a basket of acceptable Treasuries (or other eligible bonds) rather than a specific CUSIP — the opposite of specials.
Hallucination is fluent generation that is not supported by the source or the world — a likelihood-trained model completing a pattern, not a database lookup.
Household Savings Rate — Aggregate saving that supports or constrains future consumption and risk asset demand.
Idiosyncratic risk is residual variance after the factors — name-specific noise that diversification is supposed to shrink.
Implementation shortfall is the gap between a decision price (or arrival price) and the actual average execution price, including missed-trade opportunity cost.
Opportunity cost is the value of the next-best alternative you give up when you choose one use of a scarce resource — time, capital, balance-sheet, or a risk limit.
Private Equity Dry Powder — Undeployed PE capital that can support LBO activity and credit demand.
PPO is a policy-gradient algorithm that clips the probability ratio so each update stays close to the previous policy, giving much of TRPO’s stability with first-order SGD.
Rising Stars — High-yield upgrades into investment grade, often supporting spread tightening episodes.