DCA vs 일시불 시뮬레이터 계산기

무료 계산기: 역사적으로 일시불 vs 정액 분할 투자를 비교하세요.

Lump Sum

DCA Period (Months)

Dataset

결과

85.59%

8.86%

4.71%

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$1$50

작동 방식

Enter lump sum amount, DCA period in months, and select an index dataset. The tool runs sliding window simulations comparing lump sum vs dollar cost averaging. Select S&P 500 or MSCI World dataset, enter $10,000 lump sum versus 12 month DCA, and review win rate across hundreds of rolling historical windows. Each window slides one month forward for robust comparison. Win rate above 60% on S&P 500 dataset supports lump sum for long horizon investors with stable employment income as backup. Select S&P 500 or MSCI World dataset, enter $10,000 lump sum versus 12 month DCA, and review win rate across hundreds of rolling historical windows. Each window slides one month forward for robust comparison. Enter lump sum amount, DCA months, expected return and volatility: tool compares terminal wealth distributions for both strategies. Select S&P 500 or MSCI World dataset, enter $10,000 lump sum versus 12 month DCA, and review win rate across hundreds of rolling historical windows. Each window slides one month forward for robust comparison. Enter lump sum amount, DCA months, expected return and volatility: tool compares terminal wealth distributions for both strategies. Select S&P 500 or MSCI World dataset, enter $10,000 lump sum versus 12 month DCA, and review win rate across hundreds of rolling historical windows. Each window slides one month forward for robust comparison.

Review lump sum win rate, average returns, best and worst outcomes, and return distribution buckets showing when each strategy wins. Distribution buckets show how often each strategy wins by margin size. A 55% lump sum win rate with large average advantage suggests expected value favors immediate investment. Best and worst columns show tail outcomes. If worst lump sum loss exceeds your risk budget, lengthen DCA period. Switch from 6 month to 18 month DCA period on same dataset to see win rate sensitivity interactively. Distribution buckets show how often each strategy wins by margin size. A 55% lump sum win rate with large average advantage suggests expected value favors immediate investment. Historical bias favors lump sum in long US data but DCA reduces regret risk when entry coincides with immediate drawdown. Distribution buckets show how often each strategy wins by margin size. A 55% lump sum win rate with large average advantage suggests expected value favors immediate investment. Historical bias favors lump sum in long US data but DCA reduces regret risk when entry coincides with immediate drawdown. Distribution buckets show how often each strategy wins by margin size. A 55% lump sum win rate with large average advantage suggests expected value favors immediate investment.

보수적 및 낙관적 시나리오로 가정을 테스트하세요. 다양한 입력값 간 결과를 비교하세요. 결정을 내리기 전에 민감도 분석을 실행하세요.

입력값이 변경될 때마다 DCA vs 일시불 시뮬레이터을(를) 사용하세요: 시장 변동, 새로운 기여금 또는 수정된 개인 가정 후. 소프트웨어 설치 없이 빠른 재실행을 위해 페이지를 북마크하세요.

단계별 안내

DCA vs 일시불 시뮬레이터을(를) 열고 현재 입력값을 입력하세요.
계산된 출력과 요약 테이블을 검토하세요.
가정을 조정하고 시나리오를 나란히 비교하세요.

실전 예제

Example scenario for DCA vs Lump Sum Simulator: $10,000, 60%, $10,000. Enter those values above to reproduce the walkthrough described in How it works.

한 번에 하나의 입력을 조정하여 민감도를 확인하세요. DCA vs 일시불 시뮬레이터은(는) 즉시 업데이트되므로 행동하기 전에 낙관적 및 보수적 가정을 스트레스 테스트할 수 있습니다.

이 계산기를 사용할 때

Reach for DCA vs Lump Sum Simulator when compare lump sum vs dollar cost averaging historically.. It suits quick what if analysis before trades, allocation changes, or plan updates.

결정이 세금, 유동성 또는 하나의 공식이 포착하는 것 이상의 다년 전망을 포괄할 때 관련 도구와 함께 사용하세요.

흔한 실수

입력 단위나 오래된 시장 가격을 확인하지 않고 출력을 복사하는 것은 DCA vs 일시불 시뮬레이터에서 흔한 오류입니다. 행동하기 전에 티커, 백분율 및 날짜를 확인하세요.

단일 기준 시나리오만 실행하면 꼬리 위험을 무시합니다. 보수적 입력으로 스트레스 테스트하고 결정이 중요할 때 아래 나열된 관련 도구와 비교하세요.

공식

Lump Sum Final = Principal × Π(1 + r_i) over period t. DCA Final = Σ(Installment × Π(1 + r_{i..t})) for each installment i. Lump Win Rate = % of windows where Lump > DCA.

Sliding window simulation over seeded monthly return data. 5 index datasets with 240 monthly returns each. Sliding window uses monthly returns without transaction costs. Add estimated fees mentally for small frequent DCA purchases. Single path simulation not full Monte Carlo distribution: rerun with different return assumptions to bracket outcomes. Sliding window uses monthly returns without transaction costs. Add estimated fees mentally for small frequent DCA purchases. Single path simulation not full Monte Carlo distribution: rerun with different return assumptions to bracket outcomes. Sliding window uses monthly returns without transaction costs. Add estimated fees mentally for small frequent DCA purchases.

제한 사항 및 가정

주요 용어

How does the simulation work: The tool slides a window of DCA months across historical monthly return data, comparing lump sum (all invested at once, compounded) vs DCA (equal installments, each compounded from its investment date to period end).
Which datasets can I compare: S&P 500, MSCI World, MSCI EM, US Bonds, and US Small Cap.
모델 가정: Historically, lump sum wins about 60-70% of the time because markets trend up over time and being fully invested captures more of the upward drift.

대안 비교

Explore more free calculators on portfolios. Use those calculators when dca vs lump sum simulator alone does not capture the full decision.

portfolios.tools의 내부 링크는 계산기 체인을 도와줍니다: 먼저 DCA vs 일시불 시뮬레이터을(를) 실행한 다음, 아래 관련 섹션의 전문 도구로 엣지 케이스를 검증하세요.

FAQ

How does the simulation work?

The tool slides a window of DCA months across historical monthly return data, comparing lump sum (all invested at once, compounded) vs DCA (equal installments, each compounded from its investment date to period end). Synthetic 240 month return series approximate index behavior for educational simulation. Actual fund returns include dividends and fees not identical to index data. US Small Cap dataset shows higher vol and slightly higher DCA win rate versus large cap over selected windows. Synthetic 240 month return series approximate index behavior for educational simulation. Actual fund returns include dividends and fees not identical to index data. Twelve month DCA splits entry across paychecks psychologically even when mathematically suboptimal versus immediate invest. Synthetic 240 month return series approximate index behavior for educational simulation. Actual fund returns include dividends and fees not identical to index data. Twelve month DCA splits entry across paychecks psychologically even when mathematically suboptimal versus immediate invest. Synthetic 240 month return series approximate index behavior for educational simulation. Actual fund returns include dividends and fees not identical to index data.

Which datasets can I compare?

S&P 500, MSCI World, MSCI EM, US Bonds, and US Small Cap. Each has 240 months of seeded synthetic returns generated to approximate real index behavior. Bond heavy periods favor DCA when equities decline after initial lump sum deployment. Switch datasets to see asset class sensitivity. MSCI EM dataset shows higher vol and more frequent DCA wins during emerging market drawdown windows. Bond heavy periods favor DCA when equities decline after initial lump sum deployment. Switch datasets to see asset class sensitivity. Volatility input raises value of waiting: high vol environments widen DCA versus lump sum outcome gap in simulations. Bond heavy periods favor DCA when equities decline after initial lump sum deployment. Switch datasets to see asset class sensitivity. Volatility input raises value of waiting: high vol environments widen DCA versus lump sum outcome gap in simulations. Bond heavy periods favor DCA when equities decline after initial lump sum deployment. Switch datasets to see asset class sensitivity.

Why does lump sum usually win?

Historically, lump sum wins about 60-70% of the time because markets trend up over time and being fully invested captures more of the upward drift. DCA wins in declining or highly volatile markets. Lump sum wins more often because upward drift dominates long samples. DCA still wins meaningful minority of windows during bear markets. Bond dataset windows in rising rate periods occasionally favor DCA when early months face price declines. Lump sum wins more often because upward drift dominates long samples. DCA still wins meaningful minority of windows during bear markets.

What scenarios does DCA protect against?

Results show average, best, and worst outcomes for both strategies. Even if lump sum averages higher, DCA sometimes wins, especially during bear market windows. Check worst case DCA vs worst case lump sum. Worst case lump sum paths often coincide with major crash entry points like 2000 or 2008. DCA smooths entry during those windows. Use worst lump sum outcome as stress case for windfall timing anxiety discussion with spouse or advisor. Worst case lump sum paths often coincide with major crash entry points like 2000 or 2008. DCA smooths entry during those windows. Cash drag during DCA months hurts if cash yield trails expected equity return: see Cash Drag Estimator for magnitude. Worst case lump sum paths often coincide with major crash entry points like 2000 or 2008. DCA smooths entry during those windows. Cash drag during DCA months hurts if cash yield trails expected equity return: see Cash Drag Estimator for magnitude. Worst case lump sum paths often coincide with major crash entry points like 2000 or 2008. DCA smooths entry during those windows.

How does DCA period length affect results?

Longer DCA periods (12+ months) increase DCA's chance of winning. Shorter periods (3-6 months) favor lump sum because less time is spent out of the market. Short 3 month DCA barely differs from lump sum. 24 month DCA increases time out of market and raises DCA win frequency modestly. Twelve month DCA on windfall is common compromise between math and sleep at night comfort. Short 3 month DCA barely differs from lump sum. 24 month DCA increases time out of market and raises DCA win frequency modestly. Hybrid invest half now and DCA half over six months balances regret and timing risk for anxious accumulators. Short 3 month DCA barely differs from lump sum. 24 month DCA increases time out of market and raises DCA win frequency modestly. Hybrid invest half now and DCA half over six months balances regret and timing risk for anxious accumulators. Short 3 month DCA barely differs from lump sum. 24 month DCA increases time out of market and raises DCA win frequency modestly.

이 DCA vs Lump Sum 계산기를 휴대폰이나 태블릿에서 어떻게 사용하나요?

네. DCA vs Lump Sum 계산기는 최신 모바일 브라우저에서 데스크톱과 동일한 공식으로 작동합니다. 선택적 localStorage로 입력값을 기기에 저장할 수 있습니다.

DCA vs 일시불 시뮬레이터을(를) 사용할 때 내 데이터는 어디에 저장되나요?

당사 서버 어디에도 없습니다. 계산은 브라우저에서 로컬로 실행됩니다. 선택적 localStorage는 기기에서만 양식 필드를 저장하며 네트워크를 통해 포트폴리오 번호를 전송하지 않습니다.

세금 또는 법적 결정에 DCA vs 일시불 시뮬레이터에 의존해야 하나요?

아니요. 이 도구는 교육용 수학만 제공합니다. 세법, 계좌 규칙 및 개인 상황은 다양합니다. 중대한 결과를 초래하는 거래 전에 자격을 갖춘 세무 또는 법률 전문가와 상담하세요.