All Articles: 50 Topics in Japanese Equity Research
Browse all 50 articles covering J-Quants, backtesting, factors, strategies, portfolios, data and risk.
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BacktestingLearn what historical tests establish, then inspect information timing, costs, simulated fills and unused evaluation periods.FactorsStudy feature definitions, IC, quantiles, missingness and correlation while keeping predictive diagnostics separate from trading results.Risk and returnRead growth, variability and losses separately, with consistent periods and calculation conventions.Portfolio constructionSeparate target allocations from achieved holdings and inspect rebalancing, concentration, trading and costs.Research dataInspect availability timestamps, price and dividend conventions, and the identity of preserved research inputs.J-QuantsUnderstand J-Quants prices, financial disclosures and listed issues, then validate and preserve inputs for Japanese-equity research.Strategy conceptsStudy momentum, moving averages, value, quality and other characteristics as hypotheses requiring explicit evaluation.
Backtesting
What Is Backtesting? What Historical Tests Can and Cannot Tell YouBacktesting applies trading rules to historical data. Learn how to read orders, simulated fills and skipped orders, and understand the assumptions behind the results using a public KabuForge example.What are transaction costs? Why fees change backtest resultsUnderstand fees, spreads, slippage and turnover, and learn how to interpret results after costs.What is lookahead bias? Using information that was not yet knownLearn how lookahead bias arises from information timing and the misuse of future evaluation labels.Your First KabuForge Backtest: From Inputs to ResultsUse the synthetic offline demo to learn inputs, validation, execution and result inspection before moving to real local prices.What Is Out-of-Sample Evaluation? Handling Periods You Have Already InspectedSeparate development and evaluation periods, understand how repeated inspection changes their interpretation, and keep a record of rules and data versions.What Daily Bars Cannot Tell You: Highs, Lows and Execution OrderIdentical daily OHLC values can hide different intraday paths. Learn why touching a price does not establish execution and how to state backtest assumptions.What is turnover? Distinguish one-sided and two-sided measuresCalculate turnover from fictional trades and check numerator, denominator and aggregation.What is slippage? Separate reference prices from fillsDistinguish price differences, fees, execution delays and actual fills with fictional trades.What is survivorship bias? The problem with today’s stock listUse fictional securities to examine the difference between survivors and the historical universe.Historical Paper Replay and Forward Testing: Different EvidenceSeparate replayed history, newly observed forward records and real trading evidence.
Factors
What Is a Factor? Comparing Security Characteristics with NumbersLearn what a factor measures, how scores differ from rankings and trading orders, and why factor evaluation does not establish investment profitability. Public KabuForge examples keep features separate from future-return labels.What Is IC? Reading the Relationship Between Factors and Future ReturnsCompare IC and Rank IC using fictional data, and learn how labels, missing values, sample size and costs affect interpretation.What Is Quantile Analysis? Evaluating Factors by GroupsGroup securities by factor score and examine future returns, group sizes, missing data and membership changes using an explicitly fictional example.Factor Coverage: Check Which Securities and Dates Reach EvaluationSeparate eligible securities, valid features and future labels before interpreting changes in the evaluation sample.Rank Normalization: Comparable Scales Still Need Clear ConventionsCompute an illustrative rank transformation and inspect ties, missing values, direction and weights.Factor Correlation: Check Whether Features Repeat the Same InformationDistinguish feature-to-feature correlation from IC and check the common sample and invalid observations.ICIR: What the Mean and Variation of IC Can Tell YouCalculate an illustrative mean-to-standard-deviation ratio and inspect sample size, overlapping labels and inferential limits.
Risk and return
What is maximum drawdown? Measuring the fall from a portfolio peakLearn how maximum drawdown is calculated and how to read an underwater chart using a short portfolio-value example.Read Backtest Results: Separate Returns, Risk and TradingRead reports in order: input identity, equity, drawdowns, fees, orders and position cycles, with explicit metric conventions.What Is the Sharpe Ratio? Why the Number Alone Is Not EnoughUnderstand excess returns, volatility, sampling frequency and annualization, and learn why a Sharpe ratio needs context before comparison.CAGR: separate compound growth from the path of lossesCalculate compound annual growth with fictional values and no external cash flows.Volatility: align return frequency and standard-deviation conventionsCalculate fictional daily volatility with explicit ddof and annualization assumptions.Sortino ratio: define the target and downside denominatorUse fictional monthly observations to define target shortfall and the full-sample denominator.
Portfolio construction
Research data
What is point-in-time data? Understanding historical availabilityDistinguish observation dates, publication times and revisions, and understand why frozen data is not necessarily point-in-time data.Price return and total return: check the benchmark basisUse a fictional dividend example to distinguish price return from total return.Reproduce a Study: Preserve Data, Settings and Run EvidenceBuild a reviewable evidence package linking input identity, recipes, software and receipts.Ask an AI Agent to Help Install KabuForge: Scope and ChecksDefine the selected version, installation paths and approval scope, then verify what actually ran.MCP Research Assistance: Define Inputs and PermissionsCheck a bounded workspace and the selected build's tool schemas, then separate inspection, research and Paper mutations.
J-Quants
What to check first in J-Quants price dataInspect existing local price data for dates, price conventions, duplicates, trading halts and freeze records.Use J-Quants Data in KabuForge: Your First Research RunSelect, validate and freeze existing local price data, run a public teaching rule, and check the input identity and results.What Is J-Quants? Research Uses and LimitsJ-Quants provides Japanese market data for individual users.J-Quants Price Data: Fields and Observation DatesA daily bar represents one security on an observation date.J-Quants Financial Data: Periods and UnitsFinancial data has a reporting period and a disclosure timeline.J-Quants Disclosures: Times and Financial RevisionsA reporting period ends before its results are published.Japanese Equity Backtests with J-Quants: Input DesignDefine what information drives a decision, when that decision occurs and when execution follows.J-Quants Adjusted Prices and Stock SplitsIn a fictional two-for-one split, a price moving from 2,000 to 1,000 alongside doubled shares does not itself imply a halving of holding value.J-Quants and Point-in-Time ResearchPoint-in-time research checks whether every input was available by the corresponding decision cutoff.Factor Analysis with J-Quants Data: Inputs and EvaluationFactor analysis turns security characteristics into values and evaluates their relationship with later returns.J-Quants Listed Issues and Market SegmentsA security master changes with listings, delistings and classifications.J-Quants Data Storage and Reproducible ResearchReproduction requires matching input content, selection rules, transformations and software versions.
Strategy concepts
Test Japanese Equity Momentum with a Public SampleInspect the public 20-session momentum lesson through frozen inputs, prior-close signals, next-session open fills and skips.What is momentum? Separate a price trend from a rankingUse fictional prices to distinguish a security’s own trend from a cross-sectional ranking.What is a moving average? Check its window and availabilityCalculate a simple moving average and distinguish warm-up gaps, observation time and execution.Value investing: separate valuation measures from future returnsDefine a valuation characteristic with fictional book equity and market capitalization.Quality factors: fix a profitability definition before comparing firmsUse fictional earnings and equity to study profitability as one dimension of quality.Size factors: distinguish market capitalization from a return spreadDistinguish a company’s market capitalization from a portfolio return spread.Low volatility: measure historical variation without promising safetyUse return dispersion to distinguish a low-volatility characteristic from future safety.Multifactor research: check scale and overlap before combining scoresCombine fictional normalized scores while preserving direction, timing and validation rules.
Sources and updates
Applies to: main source candidate (unpublished) · Public source docs/en_US/GUI_RESEARCH_COURSES.md
Reviewed · KabuForge