本文針對中心化加密貨幣交易所（CEX）所推出之「新幣挖礦」（Launchpool）計畫進行實證分析︒本文於 2020 年 9 月至 2025 年 9 月期間，自 Binance︑Bybit︑Gate.io︑KuCoin 四家主要交易所手工蒐集 929 筆 pool-level 觀察值, 並結合一分鐘 OHLCV 高頻交易資料與CoinMarketCap 五分鐘 USD 報價, 記錄三項實證發現︒整體論點圍繞新幣挖礦之雙幣機制: 一方面, 投資人須先買入質押幣方能參與, 於質押幣上形成需求面溢出;另一方面, 獎勵幣係以零成本基礎獲得, 相較於 ICO/IEO 投資人以正成本基礎買入代幣,形成「成本基礎不對稱」（cost-basis asymmetry）︒此二者分別於質押幣與獎勵幣上衍生方向相反之報酬訊號︒第一, 新幣挖礦公告於「質押幣」上產生統計上與經濟上皆顯著之事件期累積平均異常報酬（CAAR）: 在 [−5, +10] 小時視窗下 CAAR 為 +0.55%, 於1% 水準下顯著, 且事件期前後皆無明顯漂移, 與 Cong et al. (2021) Tokenomics 框架所預測之需求面溢出效應一致; 橫斷面 OLS 迴歸（含年度固定效果與 HC3 穩健標準誤）顯示事後實現之 ln(質押總額/流通供給量) 為 CAR 之正向因子（係數 +0.0008, 於 1% 水準下顯著）, 挖礦期限則為負向因子（係數−0.0005, 於 1% 水準下顯著）, 兩者方向相反但互相一致︒第二, 新發行之獎勵幣於可交易後出現強烈且持續之市場調整買入持有異常報酬: 一週−5.66%︑一個月−15.30%︑三個月−30.63%, 三 horizon 平均皆於 1% 水準下顯著為負; 報酬下行幅度與「上市時點相對挖礦期之關係」呈單調對應: 活動結束後始上市之新幣於一個月後下跌 28.13%, 而於活動展開前已上市之新幣同期僅跌 12.35%︒其方向恰與 Benedetti and Kostovetsky (2021) 文獻所記錄之 ICO 上市首日 BTC 調整後+15%︑Sun and Yang (2024) 所記錄之 IEO 上市首日 +11% 相反, 本文將此符號逆轉歸因於成本基礎不對稱︒第三, 將前兩項發現整合為一可實作交易策略—於公告時點買入質押幣︑活動中段以期貨對沖出場︑reward 於可分發且可交易之首刻立即出清—進行績效檢驗: 全樣本下每事件平均報酬為 +1.29%（於 10% 水準下邊際顯著）, 獲利集中於跨幣質押之平台幣池（+3.13%, 於 1% 水準下顯著）與穩定幣池（+0.63%, 於 1% 水準下顯著） ; 自質押（self-stake）情境下每事件平均報酬為−0.65% （與零無統計差異） , 符合投資人於質押標的與獎勵標的兩端皆暴露於同一零成本基礎供給壓力之預測︒穩健性檢驗在 Shen et al. (2020) 三因子加密貨幣定價模型︑五組替代事件視窗︑Boehmer et al.(1991) 變異數調整檢定與 Cowan (1992) 廣義符號檢定︑以及早期 vs. 近期次樣本切分下皆維持經濟上與統計上相似之結論︒

This thesis empirically examines launchpool programmes on centralised cryptocurrency exchanges. Using 929 pool-level events across Binance, Bybit, Gate.io, and KuCoin from September 2020 through September 2025, we document three sets of return patterns and organise them around a single underlying structure: the launchpool’s dual-token design, in which the staking requirement generates a demand-side spillover on the staked token, while the reward token is distributed at zero cost basis—a cost-basis asymmetry relative to paid-in (ICO/IEO) mechanisms—so that the two channels produce opposite-signed return signals. First, the announcement of a launchpool generates a cumulative average abnormal return of +0.55% on the staking token in the [−5, +10] hour window, significant at the 1% level; cross-sectional regressions with year fixed effects and HC3 standard errors identify the realised stake-to-supply ratio as a positive correlate of the event-window CAR (coefficient +0.0008, significant at the
1% level) and mining duration as a negative correlate (coefficient−0.0005, significant at the 1% level). Second, newly issued reward tokens decline systematically in the post-event period (−5.66% at one week,−15.30% at one month,−30.63% at three months relative to BTC), each significant at the 1% level; the magnitude of the decline orders monotonically by list-
ing timing relative to the staking period, with tokens listed after the staking period ends declining by−28.13% at one month versus−12.35% for tokens listed before the staking period begins. The sign of the post-listing return reverses relative to Benedetti and Kostovetsky (2021)’s ICO first-day BTC-adjusted +15% and Sun and Yang (2024)’s IEO first-day +11%
benchmarks; we attribute the reversal to cost-basis asymmetry. Third, an implementable strategy combining the two channels—buying the staked token at announcement, hedging the position at the staking-period midpoint via a perpetual short, and selling received reward tokens at
T0 = max(tend, tlisting)—yields a per-event return of +1.29%, marginally significant at the 10% level, with profitability concentrated in cross-staking pools using platform tokens (+3.13%) or stablecoins (+0.63%), both significant at the 1% level. The self-staking subgroup is statistically
indistinguishable from zero (−0.65%), consistent with the hypothesised compounding of zero-cost-basis exposure across both legs of the position. Under the Shen et al. (2020) three-factor model the announcement event-window CAAR is +0.43% (significant at the 1% level); the result also survives the Boehmer et al. (1991) variance-adjusted BMP test, the Cowan (1992) generalised sign test, alternative event-window constructions, and an early- vs. recent-period sub-sample split.

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