The healthy operation of digital finance is inseparable from effective supervision. At present, the blockchain still has certain problems in digital financial supervision. Blockchain technology can improve the security and controllability of the blockchain system by controlling membership, but it cannot ensure that all parties will not conspire to tamper with the basic agreement and ultimately harm the interests of other participants. This article takes the impact of high-performance computing and blockchain technology on the information disclosure and prediction of stock issuance as the research object, and selects the stock and stock index data disclosed by the stock issuance information of 100 listed companies on the Google Finance platform as samples, including daily opening prices, highest price, lowest price, closing price, transaction volume and adjusted closing price, among which the adjusted closing price is used as the target variable. The stock price data and transaction volume data are taken as input feature parameters at the same time, and normalized, and a stock prediction model suitable for different situations is established: CNN network model based on the distributed mechanism of blockchain technology to predict the stock issuance. The research results show that only when the application cost of blockchain technology is controlled within a certain range, that is, when the BC is 36.01, can the losses caused by the many drawbacks of the traditional model be compensated, thereby promoting the active use of the technology in the securities market. As far as each node company in the securities market is concerned, blockchain technology has helped stock issuers to increase their income most significantly, so they can bear more technology application costs.

Xiaoling Wang. Disclosure and Forecast of Stock Issuance Information Based on High Performance Computing and Blockchain Technology. International Journal of Multimedia Computing (2023), Vol. 4, Issue 1: 1-19. https://doi.org/10.38007/IJMC.2023.040101.

[1] Miller, E. M. Risk, Uncertainty, and Diver-gence of Opinion .Journal of Finance, 2020, 7(32): 1151-1168.

[2] Diamond, D. W. and Verrecchia, R. E. Con-straints on Short-Selling and Asset Price Adjustment toPrivate Information.Journal of Financial Economics, 2020, 7(18): 277-311.

[3] Morck R., Yeung B., Yu W. The InformationContent of Stock Markets:Why Do Emerging MarketsHave Synchronous Stock Price Movements? .Journalof Financial Economics, 2019, 9(58): 215-260.

[4] Scheinkman, J. A. and Xiong, W. Overconfi-dence and Speculative Bubbles .Journal of PoliticalEconomy, 2020, 4(1): 1183-1220.

[5] Li J., Myers S. C. Around the World:NewTheory and New Tests .Journal of Financial Eco-nomics, 2016, 79(2): 257-292.

[6] Bris, A., W. N. Goetzmann, N. Zhu. Efficiencyand the Bear:Short Sales and Markets around theWorld.Journal of Finance, 2018, 9(62): 1029-1079.

[7] Boulton, T. J., M. V. Braga-Alves. The Skin-ny on the 2008 Naked Short Sale Restrictions .Jour-nal of Financial Markets, 2019, 5(13): 397-421.

[8] Ghysels E, Santa-Clara P, Valkanov R. There is a risk-return trade-off after all. Journal of Financial Economics. 2020, 76(3): 509-548. 

[9] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility. Journal of financial Economics. 2020, 19(1): 3-29. 

[10] Guo H, Whitelaw R F. Uncovering the risk–return relation in the stock market. The Journal of Finance. 2016, 61(3): 1433-1463. 

[11] Salvador E, Floros C, Arago V. Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?. Journal of Empirical Finance. 2019, 28(4): 60-77. 

[12] Brandt M W, Kang Q. On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach. Journal of Financial Economics. 2019, 72(2): 217-257. 

[13] Rapach D E, Wohar M E. Multi-period portfolio choice and the intertemporal hedging demands for stocks and bonds: International evidence. Journal of International Money and Finance. 2019, 28(3): 427-453. 

[14] Glosten L R, Jagannathan R, Runkle D E. On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance. 2020, 48(5): 1779-1801. 

[15] Turner C M, Startz R, Nelson C R. A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics. 2020, 25(1): 3-22. 

[16] Black F, Cox J C. Valuing corporate securities: Some effects of bond indenture provisions. The Journal of Finance. 2020, 31(2): 351-367. 

[17] Li Q, Yang J, Hsiao C, et al. The relationship between stock returns and volatility in international stock markets. Journal of Empirical Finance. 2019, 12(5): 650-665. 

[18] Hatemi-J A, Irandoust M. The dynamic interaction between volatility and returns in the US stock market using leveraged bootstrap simulations. Research in International Business and Finance. 2019, 25(3): 329-334. 

[19] Smith L V, Yamagata T. Firm level return–volatility analysis using dynamic panels. Journal of Empirical Finance. 2019, 18(5): 847-867. 

[20] Theodossiou P, Savva C S. Skewness and the relation between risk and return. Management Science. 2016, 62(6): 1598-1609. 

[21] Whitelaw R F. Time variations and covariations in the expectation and volatility of stock market returns. The Journal of Finance. 2019, 49(2): 515-541. 

[22] Backus D K, Gregory A W. Theoretical relations between risk premiums and conditional variances. Journal of Business & Economic Statistics. 2020, 11(2): 177-185. 

[23] Ghysels E, Guérin P, Marcellino M. Regime switches in the risk–return trade-off. Journal of Empirical Finance. 2019, 28: 118-138. 

[24] Andreou E. On the use of high frequency measures of volatility in MIDAS regressions. Journal of Econometrics. 2016, 193(2): 367-389. 

[25] Bollerslev T, Gibson M, Zhou H. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities. Journal of Econometrics. 2019, 160(1): 235-245.