Quant LetterNo. 59
154 items across 10 sections, as sent to readers on 31 July 2024. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
20 items
The article introduces a deep learning framework for options hedging that enhances performance by learning the differences between the hedging function and the Black-Scholes delta, using less data for effective results.
8 shares1 citation todaySource ↗
The study offers a unified framework for calculating CVA sensitivities, hedging the CVA, and evaluating CVA risk using probabilistic machine learning, while also identifying the best practical trade-offs.
5 shares1 citation todaySource ↗
The research investigates the consumption-investment problem in an incomplete market with unlimited parameters, using the martingale optimal principle and quadratic backward stochastic differential equations to derive optimal strategies.
3 shares6 citations todaySource ↗
The paper explores the Sample Average Approximation procedure within a biased Monte Carlo estimator framework, using Multilevel Monte Carlo to improve computational efficiency and demonstrating uniform convergence and sample complexity.
3 shares2 citations todaySource ↗
The study presents set risk measures, which extend traditional risk measures to sets of random variables, and establishes an axiom scheme for them, demonstrating their use in systemic risk, portfolio optimization, and decision-making under uncertainty.
2 shares5 citations todaySource ↗
The article explores the difficulties of incorporating AI into businesses, proposing dynamic regulation and innovation ecosystems as solutions, with Fintech as a case study.
3 shares27 citations todaySource ↗
The research investigates the evolution of Japan's labor market from 1972 to 2024, revealing a decrease in matching efficiency and a disparity in job categories.
2 sharesSource ↗
The study scrutinizes the effect of amenity density on consumer purchasing habits in Seoul, emphasizing the persistence of local shopping trends despite economic upheavals.
2 shares1 citation todaySource ↗
The study investigates the influence of unemployment benefits on job quality and stability, indicating that prolonging benefits may not enhance job-matching quality and might hinder the efficiency of job-finding social networks.
2 shares1 citation todaySource ↗
Battery Electric Buses (BEBs), despite high initial costs, become profitable in 6-7 years and can reduce ownership costs by 30%, making them suitable for public transport in Pakistan.
2 sharesSource ↗
In Entlebuch, Switzerland, increased workloads among farmers lead to feelings of loneliness, but physical isolation is not a significant factor; shorter food supply chains are linked to less loneliness.
2 sharesSource ↗
A new metric, based on Central Place Theory, effectively captures a city's spatial structure, providing a modern tool for urban planning and regional economic strategies without privacy concerns.
2 sharesSource ↗
A bio-economic agent-based model predicts further biodiversity decline due to intensified land consolidation in France; a combination of reducing pesticide use and subsidizing small farmers is the most effective strategy for biodiversity improvement.
2 shares3 citations todaySource ↗
The article suggests a new method for testing the symmetry of optimal hedge ratios, arguing that the optimal hedge ratio for Bitcoin depends on the position. This could enhance the accuracy of hedging strategies.
4 sharesSource ↗
The article explores the derivation of short-term predictions for European and VIX option prices in local-stochastic volatility models, and tests these predictions against numerical simulations.
8 shares6 citations todaySource ↗
The paper presents a method for improving the approximation schemes of the Heston model, using different random grids to increase convergence, and validates this method with financial examples.
4 shares1 citation todaySource ↗
The study argues that convex vector-valued risk measures are not suitable for defining capital allocation rules in multi-asset markets for a variety of financial applications, including systemic risk measures.
3 shares4 citations todaySource ↗
The financial sector is undergoing a transformation due to the swift adoption of AI, which could either stabilize the system or increase financial risk, and future crises may be more severe due to AI's quick reaction to shocks.
3 shares9 citations todaySource ↗
A study reveals that a 'reference market maker' who optimizes her posted depths can achieve a near perfect solution, which is compared against other solutions using an Euler scheme or reinforcement learning techniques in a competitive environment.
3 shares11 citations todaySource ↗
A basic model of transaction price dynamics, disrupted by microstructure white noise, is expanded to include serial dependence, with new moment-based estimators proposed and compared against existing methods on simulated data.
3 sharesSource ↗
Working papers in finance and economics from SSRN.
32 items
The article explores the evolution of the betting industry through algorithmic sports exchanges, comparing it to financial markets, and questions the efficiency and regulation of this new trading approach.
7 sharesSource ↗
The research presents a dynamic hedging strategy for SP 500 options, improved by a reinforcement learning algorithm and a hybrid neural network, which surpasses traditional benchmarks in both simulation and backtesting experiments.
5 sharesSource ↗
The paper investigates the shifting relationship between stocks and bonds, the effect of inflation on asset correlations, and the potential of commodities as a dynamic hedge to enhance portfolio performance.
4 sharesSource ↗
The research uses machine learning algorithms to predict the flexural crack width in reinforced concrete beams, identifying the Extra Gradient Boosting Regressor as the most accurate, and highlights the stress in reinforcing steel as a key influencing factor.
5 sharesSource ↗
The article suggests a data-driven formula to predict the ultimate shear strength of reinforced concrete beams without shear reinforcement, which performs better than existing shear provisions and is confirmed with new experimental data.
5 sharesSource ↗
The research compares the SkewStickiness Ratio (SSR) of different stochastic and rough volatility models with the empirical market SSR for the SPX Index.
4 sharesSource ↗
The research uses a combination of volatility roughness and jumps in Bitcoin to model its dynamics, showing significant differences compared to equities and fixed income funds.
3 sharesSource ↗
The study investigates the effect of monetary policy uncertainty on mutual fund flows and fund managers' ability to generate positive alpha risk-adjusted returns during Federal Open Market Committee meetings.
2 sharesSource ↗
The paper highlights the superiority of quantum neural networks over traditional machine learning methods in monitoring geoenergy production systems, especially when dealing with limited and noisy data.
2 sharesSource ↗
The review assesses the efficiency of machine learning algorithms in data filtering in fog, edge, and IoT environments, stressing the importance of data classification and the speed increase by eliminating false and noisy data.
2 sharesSource ↗
The article emphasizes the need for strong automated security in Machine Learning Operations (MLOps) to guard against various threats, and highlights the latest tools and trends in the field.
2 sharesSource ↗
The study investigates the influence of macroeconomic factors on Bangladesh's liquidity from 1986-2021, revealing a long-term effect of consumer price index, domestic credit, and exchange rate, with domestic credit having a significant impact in both short and long term.
2 sharesSource ↗
The paper studies changes in investor behavior during quantitative tightening, indicating that investors adjust their portfolios at different rates, and forecasts an overshooting pattern when the central bank stops its bond purchase due to slow adjustment by long-term investors.
2 sharesSource ↗
During non-crisis periods, institutional holdings stabilize stock prices, but during the COVID-19 crisis, they cause prices to deviate from fundamentals, with foreign and domestic institutions having different impacts and investor protection reducing volatility.
2 sharesSource ↗
The ratio of net buyback to dividend reflects a company's cash flow sensitivity to business cycles, with assets dominated by buybacks having a positive premium due to their higher risk in hedging against consumption and wealth changes.
2 sharesSource ↗
AI techniques like machine learning and deep learning can create predictive algorithms for resource allocation in cloud systems, improving service quality and reducing costs by achieving better resource utilization and fewer service quality violations.
2 sharesSource ↗
The Cash Conversion Cycle (CCC) has a minor effect on Return on Assets (ROA) but significantly influences Earnings Per Share (EPS), with the leverage ratio significantly impacting ROA, demonstrating the complex influence of financial ratios on company profitability.
2 sharesSource ↗
The research uses machine learning to predict stocks that will not have negative returns next year, recommending a Boglehead investment approach, with XGBoost providing the best results.
4 sharesSource ↗
The study applies machine learning to explore the link between ESG performance and corporate earnings, using data from over 850 European and US firms from 2007-2021.
6 shares4 citations todaySource ↗
The Spring 2023 banking stress analysis reveals a potential $1.6 trillion loss for the U.S. banking system and identifies 20 banks at risk if half of uninsured deposits are withdrawn.
3 sharesSource ↗
A model for volatility transmission in international energy markets is presented, showing that volatility is influenced by regional and preceding volatility, with non-Gaussian innovations enhancing the model's accuracy.
2 sharesSource ↗
A study of 6000 private funds from 1980 to 2022 reveals significant variations in lifetime performance, with performance relative to public benchmarks varying based on the benchmark chosen, indicating potential benefits of diversification.
4 sharesSource ↗
Companies that issued shares during the Covid19 downturn experienced better stock performance and less financial distress, with early issuers building cash reserves and later ones increasing investments.
2 sharesSource ↗
Machine learning can identify variables for optimal portfolio choice, improving portfolio performance and reducing risk, particularly when nonlinear terms are included.
2 sharesSource ↗
A Fed nonyield shock, identified from excess volatility in the SP 500 and dollar exchange rates, significantly affects stock prices and exchange rates, indicating a dominant risk premium channel.
3 sharesSource ↗
Long-term projections suggest that the German nuclear waste fund Kenfo can meet its liabilities until 2100 with 90% certainty, even under stress, and shifting more bonds to private equity can increase safety.
2 sharesSource ↗
A strong corporate culture is negatively linked with stock market liquidity, as such firms strategically reduce voluntary disclosure to deter short-term transient investors.
2 sharesSource ↗
The research shows that using multivariate cointegration for financial arbitrage strategies can generate returns without significantly increasing risk.
2 sharesSource ↗
The paper demonstrates that a portfolio based on trend-following signal outperforms in cross-asset trading.
2 sharesSource ↗
The study reveals that the likelihood of Trump's election increases volatility in US bond and equity markets and decreases oil prices.
2 sharesSource ↗
The research shows that transition climate policies are negatively priced in clean returns and positively priced in carbon-intensive returns.
2 sharesSource ↗
The study finds that political uncertainty during the 1930s regime change in Spain predicted a decline in future investment opportunities, but firms with political connections were less affected.
2 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
30 items
Pre-COVID vs. COVID: A study reveals that market conditions pre and during COVID-19 did not significantly affect arbitrage profits, but high volatility and low liquidity during the pandemic made arbitrage unfeasible.
23 sharesSource ↗
From 2004 to 2021, multi-asset funds investing globally underperformed, but performed better during market crises, with bond-focused funds doing well in non-crisis times and equity-focused funds excelling during crises.
22 sharesSource ↗
Long-term straddle momentum, implied volatility, and illiquidity are identified as key predictors of cross-sectional foreign exchange options returns.
19 sharesSource ↗
A new model enhances the accuracy of forecasting large realized covariance matrices of returns by breaking down the return covariance matrix using standard firm-level factors and sectoral restrictions.
14 sharesSource ↗
The Factor Graphical Lasso (FGL) framework, which combines graphical models with factor structure, accurately estimates portfolio weights and risk exposure, and outperforms several major competitors in portfolio allocation.
11 sharesSource ↗
The research suggests a method merging decision trees and time series modeling to predict NFL game plays based on current quarter and score.
23 sharesSource ↗
The paper introduces a model for optimizing loans in retail banking to reduce risk and increase returns by distributing capital among various loan classes.
20 sharesSource ↗
The study emphasizes the need to analyze a variable's significance before and after adjusting for covariates in machine learning, proposing a method to measure conditional feature importance.
15 sharesSource ↗
The research presents a framework to predict the severity of marine accidents using machine learning models and a unique two-stage feature selection method.
14 sharesSource ↗
The study employs machine learning to identify factors affecting AI readiness in businesses, finding that scientific research output, internet infrastructure, and public consumption expense are crucial.
13 sharesSource ↗
The study suggests that adding size- and momentum-based cryptocurrency factors to a stock-bond portfolio can benefit investors, especially when using machine-learning strategies.
23 sharesSource ↗
The paper presents a machine learning model for classifying social media posts about risk perception, aiding in understanding human risk approach and informing communication strategies.
16 sharesSource ↗
The article introduces a machine learning methodology for creating property price indices, providing higher prediction accuracy but potentially biased estimations for small samples.
15 sharesSource ↗
The paper proposes a machine learning model for flexible truck appointment systems in smart ports, using real-time data to identify disruptions and reschedule appointments, thus enhancing port efficiency.
14 sharesSource ↗
The research uses machine learning and sentiment analysis to predict the Shanghai Composite Index, highlighting the importance of investor sentiment and exchange rates.
14 sharesSource ↗
The paper introduces a sequential random search (SQRS) for hyperparameter tuning in machine learning, which improves efficiency by discarding less effective parameter configurations early.
13 sharesSource ↗
The research uses machine learning to predict the results of standard battles in the Chinese solid-state lighting industry, indicating that strong alliances, patent experience, and marketization increase the likelihood of success.
12 sharesSource ↗
The study uses a hybrid approach combining ElasticNet regression and data extraction to predict US bank failures, showing better performance than traditional regression methods.
10 sharesSource ↗
The paper uses multiple variable splitting to solve binary classification and novelty detection problems in high-dimensional data, demonstrating competitive results against other methods and specific algorithms.
10 sharesSource ↗
FinBERT and LSTM for Stock Price Prediction: The article presents a new model that combines BERT and LSTM for predicting stock prices. This model surpasses traditional methods by including financial news sentiment analysis and technical indicators, allowing it to accurately predict significant changes in stock prices.
12 sharesSource ↗
The study assesses Brazilian equity mutual funds' performance using various models, finding that three- or five-factor models minimize market anomalies and conditional methods offer greater explanatory power.
14 sharesSource ↗
A machine learning system is created to identify financial misinformation on social media, using a unique dataset of financial news articles scrutinized by the Securities and Exchange Commission.
32 sharesSource ↗
The research explores the applicability of statistical screening methods for detecting bid-rigging cartels from Switzerland to Japan, revealing that machine learning methods can achieve high accuracy rates, but performance drops when used cross-country.
24 sharesSource ↗
The research identifies a significant connection between long-short anomaly portfolio returns and the predictability of the overall market excess return, indicating that anomaly portfolio returns can forecast market excess return.
116 sharesSource ↗
A deep learning-based factor timing strategy is suggested, which surpasses other machine learning-based portfolios in the Chinese stock market, implying that mispricing-based theory can explain factor timing.
52 sharesSource ↗
The research provides a framework for managing uncertainty in factor models, revealing that such uncertainty heightens perceived risk in stocks and disagreements on expected returns peak during market crashes.
13 sharesSource ↗
The study uses various factors to build an efficient currency portfolio, discovering that a majority of the risk in these assets does not impact their risk premiums.
21 sharesSource ↗
The article introduces a new method for high-frequency estimates of economic indicators, proving its effectiveness through a simulation and application to UK's GDP data.
10 sharesSource ↗
The paper presents AI as a tool for improved prediction in competitive markets, demonstrating that AI use can increase supply elasticity, influence equilibrium prices, and potentially benefit non-adopting firms.
7 sharesSource ↗
The research explores the effect of algorithm aversion on auditor decisions, indicating that auditors tend to disregard advice from AI systems, which could be expensive for the auditing industry and financial statement users.
73 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
16 items
Robot Learning Vision Model: Theia is a robot learning model that uses multiple pre-trained vision models, enhancing robot learning with less data and smaller models.
75 shares78 citations todaySource ↗
Chemlactica and Chemma are language models fine-tuned on a corpus of 110M molecules, excelling in generating molecules with specific properties and predicting new molecular traits.
48 shares9 citations todaySource ↗
Mimicking Human Minds for Search: MindSearch is a Large Language Model-based framework that simulates human cognitive processes for web information seeking, greatly enhancing response quality.
44 shares80 citations todaySource ↗
Visual Odometry Solution: CodedVO is a new visual odometry method that solves the scale ambiguity problem by encoding depth information into imagery, achieving top performance in various indoor settings.
24 shares2 citations todaySource ↗
The study suggests using online Radiance Fields instead of traditional reconstruction-visualization components in robotic teleoperation, providing highly maneuverable, photorealistic scenes.
20 shares27 citations todaySource ↗
The Appendable Memory system is a new AI that can learn new information after its initial programming, unlike traditional machine learning methods.
17 sharesSource ↗
RISE is a method for refining large language models to improve their responses and correct errors over time, with notable success in math reasoning tasks.
15 shares211 citations todaySource ↗
The study shows that combining real and Text-to-Speech (TTS) synthesized data can achieve high accuracy in keyword spotting tasks, saving time and cost.
15 shares7 citations todaySource ↗
The 'grokking' phenomenon, where a model's test accuracy improves after achieving 100% training accuracy, can also occur with Recursive Feature Machines, not just neural networks.
14 shares28 citations todaySource ↗
Wolf, a new video captioning framework, uses Vision Language Models to efficiently summarize information, outperforming existing methods and setting a new standard for video captioning.
9 shares6 citations todaySource ↗
ShiftAddLLM is a new method developed to speed up large language models on devices with limited resources by replacing complex multiplications with simpler operations, thus reducing memory usage and latency and enhancing model performance.
170 shares47 citations todaySource ↗
The creation of artificial consciousness in AI should take into account the structural and functional aspects of the human brain; while it may not fully replicate human consciousness, AI could potentially develop different forms of consciousness.
105 shares37 citations todaySource ↗
The u-$\mu$P scheme merges Maximal Update Parametrization and Unit Scaling techniques to make model hyperparameters size-independent and easier to train in low-precision, leading to more efficient models that function immediately in FP8.
93 shares31 citations todaySource ↗
Camera-controllable Human Image Animation: HumanVid, a new large-scale dataset for human image animation that combines real and synthetic data, has been developed by researchers, setting a new standard in the field.
60 shares74 citations todaySource ↗
ROME and MEMIT: The study unifies two model editing algorithms, ROME and MEMIT, under one concept and presents EMMET, a new batched memory-editing algorithm, demonstrating that ROME and MEMIT have the same optimization goal and capabilities.
54 shares82 citations todaySource ↗
Speculative Decoding: The paper presents Block Verification, a new verification algorithm for large language models that checks a whole block of tokens at once, offering slight but consistent speed improvements over the standard token verification algorithm without adding to code complexity.
27 shares31 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
8 items
Large language models are advancing the development of multiagent systems in large-scale simulations.
3,930 shares
A formal study has begun into the creation and examination of algorithms based on large language models.
3,775 shares
AI agents are transforming work procedures in both the consumer and business sectors.
514 shares
The first General Circulation Model combining a differentiable solver for atmospheric dynamics with machine learning components has been introduced, showing promising forecasting abilities.
319 shares
The survey discusses recent advancements in Embodied AI.
151 shares
Empowering Agents: The study introduces ODYSSEY, a framework that improves LLM-based agents' skills in Minecraft.
106 shares
The article explores the demanding process of training different versions of LLMs from scratch for various scales and sizes.
55 shares
Repositories the letter featured.
10 items
The article explores the use of machine learning and anomaly detection for predicting major stock market movements.
1,740 shares
The piece delves into the application of Bayesian modeling and probabilistic programming using Python.
8,517 shares
The article details an improved Python-based, event-driven backtester inspired by QuantStart articles.
47 shares
The article presents a new columnar data format for machine learning, developed in Rust, offering quicker data access, versioning, and compatibility with different data analysis tools.
3,650 shares
The piece talks about a Python-embedded language specifically designed to tackle convex optimization issues.
5,255 shares
The article offers tips for average people on how to participate in passive investing.
519 shares
The piece explores an automated, agentless solution for resolving software development problems.
503 shares
This article presents a compilation of interesting lists on diverse topics.
315,879 shares
The write-up details the workings of Netflix's Workflow Orchestrator, Maestro.
2,056 shares
The article presents a CLI tool that converts your codebase into a single LLM prompt with source tree prompt templifying and token counting.
1,229 shares
Industry news: funds, hiring, markets and regulation.
20 items
Magnetar Capital's Structured Credit Fund experienced a 22% increase in H2, surpassing other credit-focused hedge funds with smaller gains.
8 shares
Electronic trading firms have become the main point of interest in the current financial landscape.
5 shares
GoldenTree Asset Management has recruited Sam Friedland as Principal and Head of Real Estate Credit Origination, a newly created position.
5 shares
Hudson Bay Capital has named Mike Cromie, ex-Head of Trading Technology at Segantii Capital, as Lead Engineer at its London branch.
4 shares
Nick Bird, leader of OQ Funds Management, aims to leverage the resurging interest in Japan by introducing a new strategy centered on Asia's second-largest economy.
4 shares
Hedge funds have seen a winning streak for seven quarters, with equity and global macro funds leading and net inflows returning, says Citco data.
4 shares
Decentralized trading platform tanX saw a 70% increase in spot trading volume, processing 1 billion across three million transactions in Q2.
4 shares
Neil Batchelor of Komainu emphasizes the importance of custodial infrastructure for hedge funds as demand for digital assets increases.
3 shares
Despite a recent selloff in AI stocks, Asian hedge funds remain confident in tech companies, contributing to a strong start to the year, reports Bloomberg.
2 shares
Man Group, the world's largest listed hedge fund firm, reported strong H1 results with a positive investment performance of 11.1 billion and net inflows of 0.9 billion, surpassing the industry average by 1.8.
2 shares
The Financial Conduct Authority in the UK is continuing its legal battle against BlueCrest Capital Management for a £700m investor compensation claim.
2 shares
Pawel Serej, a key portfolio manager at Elliott Investment Management in London, has left the company.
2 shares
The Texas Teacher Retirement System, along with other institutional investors, is advocating for the inclusion of cash hurdles in hedge fund incentive fees.
2 shares
The article details the new positions of former employees of Segantii Capital.
2 shares
Bill Ackman, head of Pershing Square, has announced a new investment vehicle, Pershing Square USA, with a reduced fundraising target of $2bn, down from $25bn.
2 shares
Hedge funds are rapidly shifting their stance on European stocks due to surprisingly strong corporate earnings, marking the quickest change in ten years.
2 shares
Digital asset investment products saw a modest inflow of $245m last week, with varying results across different digital assets, according to CoinShares' report.
2 shares
Bally's Corporation, a casino and gaming operator, is to be acquired by its largest shareholder, Standard General, in a deal valued at $4.6bn, including debt.
1 shares
The assertion that 300k engineering jobs don't require coding skills may be exaggerated.
1 shares
Samik Chandarana is unexpectedly deciding to spend his time at the beach.
1 shares
Episodes on markets, quant methods and economics.
10 items
Nirav Shah from Versor Investments talks about the pros and cons of using AI and machine learning in finance, including risk reduction and profit generation.
22 shares
Mariana Menchero and Faranak Golestaneh discuss the use of foundation models for time series forecasting in a podcast, emphasizing the need for diverse datasets.
11 shares
A podcast episode predicts a positive future for gold and silver despite potential US policy changes, due to factors like fiscal deficit concerns and geopolitical risk.
6 shares
Jay Barry, Teresa Ho, and Phoebe White discuss recent trends in funding markets and their potential impact on QT, and preview the August refunding and upcoming FOMC meeting.
6 shares
Arindam Sandilya, Patrick Locke, and Benjamin Shatil discuss the future of the Bank of Japan and the Yen ahead of upcoming BOJ and Fed monetary policy meetings.
5 shares
US Corp Dynamism: Kate Moore discusses the robustness of the U.S. economy, potential market impacts of the upcoming elections, and the role of AI in investing.
5 shares
Weekend Risk: The episode explores current market complexities, the effects of earnings season volatility, and strategies for hedging weekend risk.
4 shares
Marko Papic on U.S.: Marko Papic talks about recent U.S. events and their potential impact on the financial markets' outlook and risk profile.
3 shares
Tech-Driven Deflation: Jeff Booth discusses the deflationary impact of technology, the power of decentralized systems like Bitcoin, and Bitcoin's potential to change market dynamics and human freedom.
1 shares
Can it Last?: Goldman Sachs Research's team discusses the future of consumer spending and the U.S. retail sector, highlighting potential global economic strains.
0 shares
Posts from quant and economics blogs and newsletters.
2 items
Ancestry vs. Creed: Francis Fukuyama's latest essay delves into the differing concepts of national identity, one based on lineage and the other on belief systems.
0 shares
The article discusses the distinct nature of envy, highlighting that unlike most emotions, it does not provide any real pleasure.
0 shares
Posts from quant researchers on X.
6 items
The article highlights the crucial role of linear algebra in fields like computer vision, robotics, and machine learning.
4 shares
The article presents an interview with Kris Abdelmessih, co-founder of a company, sharing his journey and insights in market making and options trading.
2 shares
The article provides a review of Hoffman's book chapter on Kalman filtering and pairs trading, discussing complex topics such as partial cointegration and reinforcement learning.
2 shares
Bongratz et al. have released a paper detailing reinforcement learning methods and their application guidelines.
1 shares
User quantitativo1 has delivered a presentation on trading Initial Public Offerings (IPOs).
1 shares
A forthcoming trend is expected to gain popularity among developers and product managers.
0 shares
