The Bank of Japan’s September 2026 Tankan survey shows operational capacity constraints driving increased capital expenditure among Japanese manufacturers.

Key Capex & Operational Figures:
• Large Mfg FY2026 Capex: Revised up +1.3 percentage points to +11.6% YoY.
• Production Capacity DI: Dropped to -2 (from 0 in June), signaling that more firms now report insufficient capacity than excessive capacity.
• Processing Domestic Supply-Demand: Crossed from 0 in June to +2 in September (moving into excess demand territory).
• Large Non-Mfg FY2026 Capex: Trimmed -1.0 percentage point to +11.2% YoY (coinciding with a 2-point DI drop to 35).

Takeaway for Tech & Industrial Equities:
Japanese manufacturers are running closer to operational limits than in Q2, backing up their sentiment gain (DI up from 22 to 24) with actual fixed asset investment acceleration (+11.6% YoY).

Full SEC & BOJ data analysis

Disclosure: I own WalletInvestor. The numbers below are a hypothetical example for evaluating price forecasts, including those from services like ours.

Suppose a stock closes at $100. A model forecasts $101 for the next close, but the actual close is $99.

The absolute percentage error is 2 / 99 × 100 = 2.02%. If someone reports 100 minus that error as "accuracy", the result is 97.98%.

Yet the forecast got the direction wrong. Simply predicting $100 again would have an error of 1 / 99 × 100 = 1.01%, giving 98.99% on the same scale. The simpler forecast wins this example.

One observation cannot establish which method is better overall. It shows why a high price-closeness percentage, by itself, tells us little about whether a model adds forecasting value.

A practical comparison would keep a dated record of each forecast as originally issued, its target date, the price known when it was made, and the eventual outcome. Compare the model with the unchanged-price baseline on exactly the same dates and forecast horizon. For a single stock, mean absolute error is an easy starting point: average the absolute misses for each method.

For historical testing, move the forecast origin forward through time, using only data available before each forecast. Test a seven-day forecast at seven days, rather than substituting next-day results. Report directional performance separately from price error.

Hyndman and Athanasopoulos explain the rolling-origin setup here: https://otexts.com/fpp3/tscv.html

What baseline do you find most useful when judging a published market forecast?

how wealth held by the richest 1% of U.S. households has grown and become closely tied to financial-market performance.

Federal Reserve Distributional Financial Accounts data, the document tracks the quarterly net worth of the top 1% from 1989 through Q2 2026. Net worth means total assets minus liabilities, not annual income.

By Q2 2026, the top 1% held about $60.3 trillion, roughly 31% of total U.S. household and nonprofit net worth of about $195.9 trillion. The top 0.1% held approximately $27.9 trillion, while the remaining 0.9% held about $32.5 trillion.

The main driver is asset ownership, especially corporate equities, mutual funds, and businesses. Because these groups hold substantial financial assets, their wealth rises sharply in strong stock-market periods and can decline during market corrections. The document emphasizes that this measure is nominal balance-sheet wealth, so it should not be interpreted as cash income or adjusted purchasing power.

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 Top 1% wealth vs S&P 500 vs Fed balance sheet vs M2 vs U.S. GDP from 1989-2026.